{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d0efed1-eca7-4470-bed1-a2242b509c12",
   "metadata": {},
   "source": [
    "# Multivariate Time Series Forecasting for Stocks with LSTM-Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa5719e3-a811-4bd8-9119-0c8f85ea3b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x2072ead8d50>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, \\\n",
    "                            mean_absolute_percentage_error, \\\n",
    "                            mean_squared_error, root_mean_squared_error, \\\n",
    "                            r2_score\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, TensorDataset, Dataset\n",
    "from torchinfo import summary\n",
    "\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e060503-7135-46f2-a4a6-299a838cd718",
   "metadata": {},
   "source": [
    "**数据预处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2afcb02b-53a3-498d-b608-5d2f0de8db71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'> <class 'str'>\n",
      "<class 'numpy.float64'> <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
      "(755, 6)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('AAPL.csv')\n",
    "print(type(data['Close'].iloc[0]),type(data['Date'].iloc[0]))\n",
    "\n",
    "# Let's convert the data type of timestamp column to datatime format\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "print(type(data['Close'].iloc[0]),type(data['Date'].iloc[0]))\n",
    "\n",
    "# Selecting subset\n",
    "cond_1 = data['Date'] >= '2021-04-23 00:00:00'\n",
    "cond_2 = data['Date'] <= '2024-04-23 00:00:00'\n",
    "data = data[cond_1 & cond_2].set_index('Date')\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4782bd75-d717-4c91-a577-07901521989c",
   "metadata": {},
   "source": [
    "**数据可视化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "10689759-0ca4-4278-9848-3942ea4c55e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HEREREVGeEXO+ioqKMjyS/CKOZzJz6nIiAPvVr36Fs846C6WlpaipqcEVV1yB3bt3+21js9mwaNEiVFVVoaSkBAsXLkRDQ4PfNkeOHMH8+fNRVFSEmpoa/OAHP4DL5UrnSyEiIiIiSpveUHaYTqk4njkRgK1ZswaLFi3C+vXrsWrVKjidTsyZMwfd3d3KNvfddx/eeOMNLFu2DGvWrEFdXR0WLFig3O92uzF//nw4HA58+umneO655/Dss8/i5z//eSZeEhERERER9UI5MQfsnXfe8fv92WefRU1NDTZt2oQZM2agvb0dS5YswQsvvIALL7wQALB06VKMHj0a69evx9SpU7Fy5Urs3LkT7733Hvr27YuJEyfi//2//4f7778fDzzwAIxGYyZeGhERERER9SI5EYAFam9vBwBUVlYCADZt2gSn04lZs2Yp24waNQqDBw/GunXrMHXqVKxbtw6nn346+vbtq2xz8cUX484778SOHTswadKkkM/ldDqzdt0EMa5sHV+24/FLHI9dYnjcksPjlzgeu8TwuCWHxy85yR4/p9MJSZLg8Xjg8XhSObSsJkmS8v+eeN0ejweSJMHpdAY14Yj1b5VzAZjH48G9996L6dOnY9y4cQDkFptGoxHl5eV+2/bt2xf19fXKNurgS9wv7gtn5cqVWT95cdWqVZkeQk7j8Uscj11ieNySw+OXOB67xPC4JYfHLzmJHj+9Xo/a2lp0dXXB4XCkeFTpsWHDBsydOxcXXXQRXnnllZDb/Pvf/8a3v/1tfOtb38Ijjzyi3N7Z2YmPP/4Yl156qXJbdXU1pk6dioceeghDhw4FAIwfPx533nkn7rzzzpjG5HA4YLVasXbt2qBeEhaLJaZ95FwAtmjRImzfvh0ff/xxWp5vzpw5MJvNaXmueDmdTqxatQqzZ8+GwWDI9HByDo9f4njsEsPjlhwev8Tx2CWGxy05PH7JSfb42Ww2HD16FCUlJSgoKOiBEfa8l19+GXfddReeeeYZdHV1oX///kHbvPTSS/jBD36Ap59+Gn/+859hMpnQ2dmJ0tJSJYmya9culJaWYu/evfjOd76D66+/Hlu2bIFOp4NWq0VBQUHM5/s2mw2FhYWYMWNG0HHt6OiIaR85FYDdddddePPNN7F27VoMHDhQub22thYOhwNtbW1+WbCGhgbU1tYq22zYsMFvf6JLotgmFIPBkPUfGrkwxmzG45c4HrvE8Lglh8cvcTx2ieFxSw6PX3ISPX5utxsajQZarVZZD0uSJFid7lQPMSaFBl1cHQS7urrwyiuvYOPGjWhoaMA//vEP/OQnP/Hb5uDBg/j000+xfPlyfPjhh3jttddwzTXXAIDy2gH5XL+8vBwDBgzAz3/+c1x//fU4cOAARo4cGbRtNFqtFhqNJuTfJda/U04EYJIk4e6778arr76KDz/8EMOGDfO7f/LkyTAYDHj//fexcOFCAMDu3btx5MgRTJs2DQAwbdo0PPzww8rCaYCc0jWbzRgzZkx6XxARERERUZpZnW6M+fm7GXnunQ9djCJj7KHHK6+8glGjRmHkyJG44YYbcO+99+LHP/6xXxC3dOlSzJ8/H2VlZbjhhhuwZMkSJQALRyymnMmyzJxoQ79o0SL861//wgsvvIDS0lLU19ejvr4eVqsVAFBWVoZbb70VixcvxurVq7Fp0yZ861vfwrRp0zB16lQAcinhmDFj8M1vfhNbt27Fu+++i//93//FokWLYDKZMvnyiIiIiIhIZcmSJbjhhhsAAJdccgna29uxZs0a5X6Px4Nnn31W2eaaa67Bxx9/jIMHD4bd54kTJ/DII49gwIABSvYrE3IiA/bXv/4VADBz5ky/25cuXYqbb74ZAPDoo49Cq9Vi4cKFsNvtuPjii/HEE08o2+p0Orz55pu48847MW3aNBQXF+Omm27CQw89lK6XQURERESUMYUGHXY+dHHGnjtWu3fvxoYNG/Dqq68CkBuKXH311ViyZIkSD6xatQrd3d2YN28eAKBPnz6YPXs2li5diu9///t++xs4cCAkSYLFYsGECROwfPnyjC5BlRMBmGgnGUlBQQEef/xxPP7442G3GTJkCFasWJHKoRERERER5QSNRhNXGWCmLFmyBC6Xy6/phiRJMJlM+Mtf/oKysjIsWbIELS0tSkkhIGfFtm3bhsWLF/vt76OPPoLZbEZNTQ1KS0vT9jrCyf6/ABERERER9Qoulwv/+Mc/8Pvf/x5z5szxu++KK67Aiy++iKuuugqvv/46XnrpJYwdO1a53+1249xzz8UHH3yABQsWKLcPGzYsaLmqTGIARkREREREWeHNN99Ea2srbr31VpSVlfndt3DhQixZsgQ2mw1VVVX4xje+EdRZce7cufjXv/7lF4BFc/z4cWzZssXvtiFDhqCioiLh1xFJTjThICIiIiKi/LdkyRLMmjUrKPgC5ABs48aNWLx4Ma688sqQbe0XLFiAt99+G01NTTE/5yOPPIJJkyb5/ffWW28l9ToiYQaMiIiIiIiywhtvvBH2vrPPPjtqb4hvfOMbuOSSS2A2mzFz5syo2x86dCiRYSaFGTAiIiIiIqI0YQBGRERERESUJgzAiIiIiIh62B9W7cGPlm+DxxN9eSXKbwzAiIiIiIh60Md7m/Dn9/fipc+PYld9R6aHQxnGAIyIiIiIqAet2H5C+flAY3danztaEwqKTyqOJwMwIiIiIqIeZHW4lZ/3N3al5TkNBgMAwGKxpOX5egtxPMXxTQTb0BMRERER9SCH26P8nK4MmE6nQ3l5OU6ePAkAKCoqCrluVr7xeDxwOByw2WzQalOXa5IkCRaLBSdPnkR5eTl0Ol3C+2IARkRERETUgxwuVQDWlJ4MGADU1tYCgBKE9QaSJMFqtaKwsLBHAs7y8nLluCaKARgRERERUQ/yC8AauyFJUlqyURqNBv369UNNTQ2cTmePP182cDqdWLt2LWbMmJFUmWAoBoMhqcyXwACMiIiIiKgHOVUliBaHG/UdNvQrK0zb8+t0upQEDrlAp9PB5XKhoKAg5QFYqrAJBxERERFRD1JnwID0d0Kk7MIAjIiIiIioB4kmHFpv1eGjq/bgsr98jKMt7FDYGzEAIyIiIiLqQSIDNrqfGQCw8XArth1rxy9X7MrksChDGIAREREREfUgkQEbWVvqd7tFtT4Y9R4MwIiIiIgInTYnJEnK9DDykpIBqzX73V5o6B2NMcgfAzAiIiKiXu7jvU2Y9NAq/GHVnkwPJS8FliAKhUYGYL0RAzAiIiKiXu6va/bB5ZHw2YGWTA8lL4k29H3NJui0vvW/GID1TgzAiIiIiHqx/Y1d+GRfMwCgodOW4dHkJ5EBM+q1qCjyrU3Fks/eiQEYERERUS/2/Pojys8NHTYGBT1ANOEw6LQoLzIqt1vZhKNXYgBGRERE1EtZHW78e9NR5Xeb04MOmyuDI8o/kiTB6ZaDWqNei/JCXwbM6mQA1hsxACMiIiLqpT7e14QOmwsDKwphLtADAE52sAwxFbYfb8eyjUeV7BcgB2D3zx2l/M429L2TPtMDICIiIqLMaLc6AQCn1JSgrs2KDlsXGjrsOLVvaZRHUjRfe+xjAIBJ1WreqNPirKGVeOjysfj56ztgYwasV2IGjIiIiKiXEgGASa9FVbEJANDcbc/kkPLOZwealZ+NOvnUe1BlEQCWIPZWzIARERER9VJ2b3e+AoNO+Vl07KPEqRuZiCyjXquB1tuCvsibFWMJYu/EDBgRERFRL6XOgJn08mmhes4SJcauCmJFAGbU+067xfpfth4IwLYebcP7uxpSvl9KHQZgRERERHnO4fLgW0s34IkP9/ndLgIFk14Ho14OCuxOBmDJ6rA5lZ9bLQ4Acgt6ocgbgPVECeLlj3+CW5/biL0NnSnfN6UGAzAiIiKiPLfiyxNYvbsRv31nt9/tdpccABQYtMr8JGbAktepauV/vNUKwD8DVtBDJYhuj6/0ceeJjpTum1KHARgRERFRnmvzZmECiWyXnAHzBmCcA5Y0dQDWavGWIKoyYIXeAMzu8vgFTcnqsDpD/kzZhQEYERERUZ5zqU7y1Q0i1BkwEwOwlOm0BQc/6gyYudCAYm8Z4ltfnkjZ87argq56rueWtRiAEREREeU5p1sddHm8/3dj0+FWAAEZMJYgJk2dARNMqgDMoNPi1nOHAQD+u6UuZc+rDsCOeUsfKfswACMiIiLKcy5VUCXmHX1/2TbsaegCAJhUGTA716ZKWqgMmLnA4Pf72AFlAFK77po6ADvUbEnZfim1GIARERER5bluVbMHi0POzryx1Zd5KdDr2IQjhTqswRmwkgL/5Xcri40AgNbu0PPzEqEOwHYcb0e3PXgclHkMwIiIiIjynLotujVE5z2TQauUINo5ByxpnSECn9KAAKyiSA7AWlIYgKn/zi6PhM8PtaRs35Q6DMCIiIiIcowkSdh3sgueGDroOVwevP7FceX3UK3PTXotuyCmkCVEAFZiCp0B67C54ExR1rE9oPPh7nquBZaNGIARERER5ZjnPj2EWX9Yg399djjqtv9cfzigBNEdFGSZDDpmwFLIEmIeXWAJYlmhARqN/HNrmGUC4hUYgLEEMTsxACMiIiLKMaLBwvoDzVG3fSEgSLM6XWjq8m/8YNJrYdLLbdGZAUueKPPsazYptwU24dBpNSgvlG9r7U7Nml2B88m6U7zQM6UGAzAiIiKiHCNK1mIpMRtVa/b73eJw42RnYADGhZhTSTQ6GVpVrNwWWIIIABXF/vPAOmxOHGzqTvh56zvkv+vgyiIAzIBlKwZgRERERDnG5V3X61CzRVlMOZyOgJboFocbJwMW6S0waNkFMYW6vIHPsD6RA7DqEjlDtvmIvB7bZY99jAse+RAHGrsSet76dnntrxHV8vMyA5adGIARERER5RinRw6S3B4pasZEzC8qNMglhha7K2QGzMQMWEr84s2d+GSfXBo6VB2AFQQHYAsnDwQA/OWDfdjf2KWUln6yP3ppaSgn2uXA+pSaEgChm4FQ5jEAIyIiIsoxIgMGRC9DFPOLBlQUApAbRAQGYFoNVE04mDVJxt8/Pqj8rC5B1IqOGypfP2Mgzh5aCavTjWueXq/cbtLFf4reZXeh0yYHXCOqS5TbKPswACMiIiLKMS6PL0u1tyFyuZqYXzSgXA7ArA43Gjv9SxBLTHpmwHpAnxKj8rM2OP6CVqvBL64cB71Wg0ZVUNycwNpg9d7sV6lJjxpv849QSw5Q5jEAIyIiIsoxTlUGbE9D+AyYzemG1dsSfVClHIB12lzKyf7FY/visWsnocZcwCYcPaDQqMMPLh6JWaP7YsZp1SG3Oa1vKe6YMdzvtkTa0p/wzv+qLStAsVEud2QTjuwUXIxKRERERFnNpWqUESkAEyfyeq0GAyvkzngdVqdSgnjV5EGYNaYvAF8JIptwpE6RUY9FF5wSdbu7LzwVBxq78c6OegBAc1ciAZicAastK0Cxt+FHt4MBWDZiBoyIiIgox7g8vgzY4RYLbCEW/gV85YcVxUZUFsnlcK0WB05625XXqNapEl0Q7U4GYKlSZNTFtF2hUYcnvzkZv15wOgCgpdse5RHBRAli/7JCXwBmZwliNmIARkRERJRjnKoslSQB+06GngcmGnBUFhlRViQv+rvzRAfqO2zQaoAhlb4mEUoTDmbAUqYwxgBMqAxYFywefhkw7/N2O1yQJCnSwygDGIARERER5Rh1F0QgfBlii0VkwAwoL5QDsAZv9uvsYZVKUAYABd429Q6XB24PT9pTocgQXwBW5W3akVgTDnkOWD9VCaIkATZmNLMOAzAiIiKiHCNKEGvNBQCAvWEzYN4ArMiIimKj330Xj631+129UDDbl6eGPs528uYCOSBO5PirM2CFBh103raLojkHZQ8GYEREREQ5RrShP622FABwsDH0YsytFt8cMJEBE+YEBGAFBp1Shthpc6Z0vBSbIlPi3QvrO+QArF9ZIbRaDaYNrwIAvLH1ROoGSCnBAIyIiIgox4gSxJF95QV3DzRFzoCp54ABwOkDypR1wdREBqbDygxYItSlm8u+My3ux5d428c73VJcywFYHW60WeSgubZMzoounDwAAPCfL45xHliWYQBGRERElGNEE47T+soZsEPNlpDztlq8J+UVxUaY9DqlK98cb+v5QOYCOQBgBiwx6uYoo7zZyXgUmXxzxuJpIS+yX0VGnfI3vHhsLYqNOhxutmDj4da4x0I9hwEYERERUY4Rc8CGVBXDqNPC4fLg0sc+xvE2//k+SgasWM5sjaothVGvxfzx/ULut9R78t5hYwYsEeoAzBDn/C/xGFEGGk8L+RNtvkWYNRp57leRUY95p8t/5+WbjsU9Fuo5DMCIiIiIcowoQTTptRhSJS+wvPNEB365Ypffdi2qJhwA8PebzsK7987A8OqSkPs1e+eJMQOWGHV3ykQCMMDXDMUSRwZMNODo5y0/FBZOHggAeGvbCb/FuymzGIARERER5RiRadHrNBhe7VvL66S3FE0QTTjE+lKVxUYM61OMcJQMmJUBWCjR5lKJv4tGA6ULYbyKvWWI8WTA1A041M4YXAEA6LS7YAmzWDelHwMwIiIiohwjShANOi0qi03K7TWl/hmQwAxYNKIJRydLEIO8/eUJjH9wJT74qiHsNk7V3yVRxd5GHF1xZMDE37kqYKkBg84XBDrjaOpBPYsBGBEREVGOUTJgWo2StVLfDsid8ezek+7ANcDC8c0BYwYs0J3Pb0anzYVbnt2IP6zc7Xef0+3Bii9PoN5bCmhIMPsFQFlE2RJHBkx0TDQFLPys0WiUIMzpZifEbMEAjIiIiCjHiLlGBp0Wt547TLldtCIHgBZv+aFRp0Wx0f/EPBxmwGLz5w/2+f3+3KeH8D/Pb8bXn/wUAGDQJ5EBE2uBxZEBs7vkYM0U4nlFNs7JOWBZgwEYERERUY4RCzHrdRr0NRfghdumAPAFXYCvA2JFsUHpjBdNoTdQszg4X0gt1Nwv9TpdK3c0eLeTf9drkylBjH8OmBiLMUTpowjAHAzAsgYDMCIiIqIcIkmSUk4mTvTLvXO82lQBWLzzvwBA6w3UPFy410+rJbgks07V8r9ctcg1ABh1KShBjCMItisliMyA5QIGYEREREQ5RL3gspjfU+Fd56vN4lSyNYEdEGMhOvcx/vJ3sKk76LajrRbl59IC/wBMn0QTDtGGvtseewlipAyYCAadLv5RswUDMCIiIqIc4lIFYOJEX2S5XB5JWUT5UJPF775YiN4R6iCPQgdgx1p9GTCr0z9YMiSRASvyliB2pSoDpmcJYrZhAEZERESUQ9SlZHpvxFRg0KHMu4hyfbsNNqcbz3xyEAAw47Q+Me9bq2UJYiiHQmXAWuQAV5IktAesm5ZUG/oEFmL2ZcCCm62wBDH75EQAtnbtWlx66aXo378/NBoNXnvtNb/7GxoacPPNN6N///4oKirCJZdcgr179/ptY7PZsGjRIlRVVaGkpAQLFy5EQ0P4dRyIiIiIspHLrS5B9J3KDSiXF+E93mbB0RYL2q1OlJr0+PrkQTHv2zcHLEWDzRMHm+UAbPHs03D7eXLXyWOtVuxv7MLkX7yHT/Y1+22fTADmK0GMIwPmDa7YBTE35EQA1t3djQkTJuDxxx8Puk+SJFxxxRU4cOAAXn/9dXzxxRcYMmQIZs2ahe5u39WK++67D2+88QaWLVuGNWvWoK6uDgsWLEjnyyAiIiJKmtPbAVGj8c3ZAoCBFXIAdqzVijrvelQDKgr9tolGbMoMmD+RARvTz4wzBlcAkOeAPfjGTqXZiZo+BSWI8cwBszvlYM0YIgBT5oAxAMsa+uibZN7cuXMxd+7ckPft3bsX69evx/bt2zF27FgAwF//+lfU1tbixRdfxG233Yb29nYsWbIEL7zwAi688EIAwNKlSzF69GisX78eU6dOTdtrISIiIkqGsgZYQKvzAd4A7HirVcmE1JYVxLVvdkEMJkmSEoAN7VMMq3du1rFWK/qUhA5qUpEBi6cLoiOGDJiDTTiyRk4EYJHY7XYAQEGB7wNGq9XCZDLh448/xm233YZNmzbB6XRi1qxZyjajRo3C4MGDsW7duogBmNPphNOZnavBi3Fl6/iyHY9f4njsEsPjlhwev8Tx2CUmW4+b1S5nXPQ6jd/Y+plNAIAjzd0webMefUtNcY1f8mbXXG5P0q87W49fvE522tHtcEOrAfqVGmBxyAFNY6cdNmfoIEmvSfx1e+MvdNljP34iA6ZF8N9NxGQ2R/ae06ZSJt93sT5nzgdgIpD68Y9/jKeeegrFxcV49NFHcezYMZw4cQIAUF9fD6PRiPLycr/H9u3bF/X19RH3v3LlShQVFfXU8FNi1apVmR5CTuPxSxyPXWJ43JLD45c4HrvEZNtxa7ACgB6S24UVK1b4bm/RANBhx6F6tDZKALTobDiMFSsOxbzvLxvlfZxsbPTbdzKy7fjFa18HAOhRYZTw3sp3IElAlUmHZrsGnbbQZYKtLU0JH7+DnfLzNbV1AYjt+HV26wBosGH9p6j70v++thYtAC02bv4CmqO9JwuWifedxWKJvhHyIAAzGAz4z3/+g1tvvRWVlZXQ6XSYNWsW5s6dG3LV8njNmTMHZrM5BSNNPafTiVWrVmH27NkwGAzRH0B+ePwSx2OXGB635PD4JY7HLjHZetz2NHQCW9ahwGTEvHkXKLcPqevAkt3r0Q0TDOZS4GQzzjtzPOadMSDmfbu3ncA/932Jqqo+mDfvzKTGKY7f+RdehOc/r4PbI+HO84cntc90kyQJZ/5yNQAXxgzqg3nzJgMARkzuxH2vbMO+Rl+/AZ1Wg1qzCcfbbOhf2xfz5k1K6Dl313fij9vXQdIZAVhjev/97IsPAKcLF808HyOqi/3ue61lM75qb8KYcadj3uSBCY0pl2Ty321HR0dM2+V8AAYAkydPxpYtW9De3g6Hw4Hq6mpMmTIFZ54pf3DU1tbC4XCgra3NLwvW0NCA2traiPs2GAxZ9aEbSi6MMZvx+CWOxy4xPG7J4fFLHI9dYrLtuEkauUmDQaf1G9ewavmCcXO3Ayc75TLF6tLCuMZu0Munhh4JKXnN9Rbgumc2Y+eJTgDAdVOHok+JKen9psu+k53KumpjBpQpx+T0QZV487vn4ck1+/HH9+TO2+YCPYb2KcbxNhtMBl3Cx6+sWJ5W0+2dAxbL+0/MASsuMAZta/L+Td3QZtX7uKdl4t9trM+XE10QY1VWVobq6mrs3bsXGzduxOWXXw5ADtAMBgPef/99Zdvdu3fjyJEjmDZtWqaGS0RERBS3423yAsCBCyybC/VKA4c9J+WAR6wpFSvRMTEVPTg2H2nDI9t0SvAF+BYMzhXtVl+J4f+cf4rffQUGHe6ddZryu7nQgCFVcvZJr01+HTC7ywN3DH8HSZJ8CzGHasLhvc2pOvaSJGHVzgbsrIstY0OplRMZsK6uLuzbt0/5/eDBg9iyZQsqKysxePBgLFu2DNXV1Rg8eDC+/PJL3HPPPbjiiiswZ84cAHJgduutt2Lx4sWorKyE2WzG3XffjWnTprEDIhEREeWU9QfkNafOGlbhd7tGo8HAikJ8Vd+pBFAlcQZgqWxD/58v6uCUNJg8uBxfHu+Aw+2BJ8cWGBOt4Ef3M6OsKHJ2Y3ifYozuJ2chK4uNEbeNpNjkW0w5lqXAXB5J+XuHakNvCNGG/um1B/Crt7/C8D7F+OD7MxMeKyUmJwKwjRs34oILfDXOixcvBgDcdNNNePbZZ3HixAksXrwYDQ0N6NevH2688Ub87Gc/89vHo48+Cq1Wi4ULF8Jut+Piiy/GE088kdbXQURERJSszw+1AACmDKsKum9AuRyACUWqk/lYaLxt6N0pCMC6vKV7c8f1xe6GLjjcHrhzNAAriXAcf3/VBPz944N48LJxqDGbUGrSY8Zp1Qk/p0mvg0GngdMtxRSAqbOKJn3wOI0hFmL+y2o5sXGgqRuSJCl/d0qPnAjAZs6cGbGhxne/+11897vfjbiPgoICPP744yEXcyYiIiLKFfXt8hI8I6pLgu4TizEL8WbAdMo6YAkOTqXLIQcvxSa9kllLRWCXTl3eAKzIGP44Lpw8EAtVzS2umBR705Nwiox6tFudMQVgDlUAFjoD5l0HTFXPqA63uh3uuN8nlJy8mgNGRERElM8kSUKbRW6wUVEcXBI3sMJ/6Zx454CJqUupKBX0ZY/0ytyy3M2ApTdAEc8XTwCm12qU46xmCJEBU/8d9jZ0Bj2GehYDMCIiIqIc0e1ww+U9eS4vDJ5nNCAgA1ZkiK8EUatkwFJXgigHYPIpZ84FYN5OhMVxlnImSzyfzRO9NNDukscYKvsFAAa9dw6YN1DzeCTldQHAlU98ioYOW1LjpfgwACMiIiLKESL7ZdRrUWAIPo1TlyAWG3XQhsiIRKJNZQmiXZQg6uBNwuReAGb3lVGmkyh5jCcDFqoDIuCbAyYC925H8OLRK3fUJzJMShADMCIiIqIc0WZxAgAqigwhGyeoSxBDlaNFowRgqShB9GZZSkx61dyy3AzAsrkEUTThEKWGgXxzwOTtukPsVJ0Ro57HAIyIiIgoR4gALFT5ISAHZoJYQDgeyhywJAMlSZKUDFiJSa9k4nItA9ZlFyWI6Q3ARAliLAGYOKbRAjBRgthldwZtc7CxO5FhUoIYgBERERHliDarXIIYbk2qZNuJp2oOmN3lgdPbda/EpMv5JhzFxnTPAYs9AyZKC8NlPAPXAesKsdMDTV2JDJMSxACMiIiIKEe0Khmw8IsCJ1MuJ07ik42TRPYLkOcz5WwA5sjMHLBiZQ5Y9IDaHSUAE805REDcFSIzeoAZsLRiAEZERESUI9q9TTjKw2TAAGDaiOAFmmMlzuGTzYCJk3yTVoJOq1HmgOXqOmDpL0GUn8/mibIhogdggXPAxGuaPKQC2x+8GADQ3O1AuyW4NJF6BlddIyIiIsoRFkf0OUm/WnA6Cgw6XHf24Lj3L0oYk81UiZN80b1dyazFEFBkk+Yub8AbIePYE0oSmAOmjxKAOQMCsGKTHiUmPfqaTWjosONAUxcmDa5IdugUA2bAiIiIiHKEXWk5Hn5OUp8SEx67dlJCmTCRqUo2UbWjrh0AYPb2ChFzy1w5FIHZXW4ca7UAAIZVF6f1ueNpQy+OqTbM/L/AOWBiXlupN4gf1kd+bQebWIaYLgzAiIiIiHKEWHQ33JpPyUpVE47lm44DACZVySf9el3utaE/2mKBR5Ln1FWXmNL63PG0oRfHVBzjQEalC6K8XadNLjUUnRaHV5cA4DywdGIARkRERJQjlEV3QyzCnAoiiZJsCeKhZvlkfmSZvB+tUtqY1G7Tar83IBnWpzjp7pLx8nVBjP68Lnd8c8COt1kBALVl8qLdw5kBSzsGYEREREQ5QpQgGsOs+ZSsVHVBFOVuIk7MxS6IR1vk8sMhVUVRtky9IjEHLJ4mHOFKEPX+c8AON8uva6j3dQ33llfub2Qr+nRhAEZERESUI+xOkQHrmXWptMocsOQCJSUr440JdCkqbUynbm/9nznNDTgAXwmiLZYmHFJ864CJAGxIlRx4DesjlyAeau6GJ4cC5FzGAIyIiIgoi0mSpMz96uk5YCKxlmy7eFHupgvIgLly6AS/p491JMVxNOFQuiBGmwPmlmBzulHXLpcgigzYoIpCGHQa2Jwe1HfYkh06xYABGBEREVEW+/XbX2H8AyvxVX2HqgtiT80BE+3ikwuURLZFLzJg2tTsN53EsS7ooWxjJMVxtKEX2cbwXRC9c8BcHhxtsUDyNhapLJZbVOp1WpQVyj932LgWWDowACMiIiLKYk+tPQC7y4O7X/jC14QjQhv6ZPi6ICa+D7dHUh4vkjLaHJwDltEMmLcE0eHRRD1mIlsZbR2w421WzH50LQB5Xpu6sUihUd5GrDNHPYsBGBEREVEO2Huyy5cB66EuiKmYq+VUtTpUShBFd8UcmgNmc/ZssBtJiWqh7WhBkdKEQxv6PWEIUZo4tMp/XbNCb5bPxgAsLRiAEREREWUx9cl4u1UuEeu5EkT5/8kEYA5VAOYrQZTHm1sZsJ4t94zEpNdCJLS6Ha6I27qUACz0/YYQdwR2diz0zjljBiw99NE3ISIiIqJMqSoxossun4Qf8bZG77kmHGKuVuL7EHOSAChBhNLcIwcCsPd2NsCo18LulIORTMwB02g0KDbp0WlzwRJlIpiYV6cPlwEL8V4JzoDJ21idDMDSgQEYERERURYT877Uen4OWPIliHqtRhWA5UYbeqvDjTuf3wSNRoOJA8sBZCYDBgDFRh06ba44MmCR29CrDQ7MgHmDTAZg6cESRCIiIqIsFjoA65lTOG0qShC941Wf+IvALtszYBaHC063BIfLg131HQB6br5dNEXessBuVQbsYFM3LvnjWvxn8zHlNrdHtPyP3IZeLTADJp7LyhLEtGAARkRERJQlXG4Pnv3kIL7ynvwDac6AaX1dEBNdjFlkwNRzj3Q50gXRqSqf7LTJmaeCDDThAIASbyt6dQbsN29/ha/qO7H4la3KbWLKXfgMWPDpfk2pye/3AmbA0ooBGBEREVGW+PemY3jgjZ245I8fKbfZ3SECsB7KyqjXkko0CSaCGPXCwLkTgKXvWEcjWtGrM2AWVYD023e+AuDLgEVrQ6+mDdiWbejTiwEYERERUZbYUdfh97skSRkpQQQSL0MMmQETJYhZPgcsZACWoQxYkTE4A1Za4Gvf8MSH+9FpcyY0Byz4ueT92pgBSwsGYERERERZotDof7Kvbune1+wrG+vpEkQg8WDJEaEE0ZPlGTBXiPFlrglHcGv4brt/Q45DTRblmIYLwNQLLgPAv78zLWgbpQSRGbC0YABGRERElCUKVCf7TrfHL/s1wduVDwCMPZYBS74EUbShN6qbcCgliImPLR1CZRsz0YYeAIq9c8C6vHPR/vTeXny4u9FvmwNNXVEzYGqnDyjDmUMrg24XXRBZgpgeDMCIiIiIsoQ6a9RqcfgHYIPKAchzfWI52U6EThWAJTpfK1QJol4JwLI7AsumDJi5wAAA6PRmvR59b0/QNgebupVMZbg5YGqBc78EUe7IEsT0YABGRERElCXsqoCrtduplPPptRqM6WcG0LMBgSYFc8BClSBqc3kOWIaacIj5Xh224HXArpjYH4A3APNmHMMFV2rhNvFlwCKvOUapwQCMiIiIKEuoS8Baun0ZMJNei0mDy1FaoMfI2tIee351CWKi07WcIdYB0+VICWI2NeEwF8oBWFeIAGzykAoAcgAmsnaxZMB0mtDbFBjZhj6d9NE3ISIiIqJ0UJ8AP756Hz472AxAnvNVXmTEJz+6MOTCuqmiLm1MtGGGrw19iCYcWZ8BCx5fQaYyYCaRAXMCAMoKDWi3yj+fNUyex3WwsVuZG6jTRh9n2BJENuFIKwZgRERERFnCqioB+3hfk+92b2Am5gX1lNS2oQ/OgLlCBDjZxBUiA9aTAW8k5kL5b91hld8TYn7WyvtmYEhVETQaeX5YfYcNQPjsllq4bcqK5OdqtTiTHjdFxxJEIiIioiyhLkG8fspg5WebMz21exqNRpkHlnAJYoR1wLI/AyaPvcIbkBj1Wr9MXjqJOWCddhccLo8yP7BvaQFMeh0GVhQCAPad7ALgv/B1OOGat9SUykscnOy0Qcryv1E+YAaMiIiIKEuITNfvr5qAhZMH4vnPjqR9DFqNBm5JSiIDJtrQq5pwKHPAsvvkXox9ZG0pxvUvQ41q7bV0U7og2pzotPkyUyXewGxYnxIcbbHiYFM3gNja0IcrQaz2BmA2pwdddhdKezjT2tsxACMiIiLKEmIOTpExM40fALkM0Y0UlyDmWBdEg06L//3amIyORcmA2VxKJ8Rio04JtIb3KcbaPb51wWIpQQwXoxUZ9Sgx6dFld+Fkp90vAJMkKWgxZ0oOSxCJiIiIsoTIgBVmNABLLlsVsgTR+2OijT3SRcxRM2So7FDN7A3APBJQ3y7P81IHRsP6FPttH0sGrNZcEPY+kQVr7LQrt31xpBUTHlyJ5z87HPvAKarMv7uIiIiICIAvAybWZcpEJkwEYIkmq0KtAyY69IVa6DibOEJk7zLFpNdCp5GP1/E2KwBfVgwIDsAizQF78obJuGhUDX54yaiw21Qr88B8Adj3lm1Fh82Fn766Pf4XQGGxBJGIiIgoS1iUEkT5FK3YpPdrzJEOWqUJR4IZMJdoQ6/ugujdZ5YHYKILYqYab6hpNBqYdIDFBTR0iAyY79R9eLV/AKaNUCZ4ybhaXDKuNuLziUYcx1otym3ZPmcvV2X+3UVEREREANQliPIpmlgLKp2SbZjh8gRnwLQ5MwcsuIFIJoklyERZoLoEsX9ZIYx63zhjWYg5kkmD5cWdP97rW/4g83nA/JQd7y4iIiKiXsztkfCHlbuVhXbFGlA/u1RuBHHHjOFpG4tWaRmf2ONFGZ8xxDpg2Z5RcXqDx2SDmVQxBgVgvoBcq9VgWJUvCxbLHLBILhxVAwDYcLAFFtV6dJR6LEEkIiIiyrB3d9Tjzx/sAwAUGLSoLpHLwS4YWYMvfjYb5UXpawsuTuQTXQ9KlCAadFrAu3yZPlcCMDF2fXbkKCJlwAB5Htjuhk4AyQdgw/oUo8iog8XhRmOnHUOq9BHLGilx2fHuIiIiIurF6rxNFgCgT4nJr+13RbExrW3AxXl8ouWCodrQ58o6YEr5ZJZkwJQArEsOwMwF/rmTYdWpy4AB/q3vAbAGsYcwACMiIiLKMHWsY3N6MjcQQAn2PAkOI2QbeqWsMbsDsFAdHDPJqJWP18kQTTgA/7byem3yYy7xzjnssssBGDNgPSM73l1EREREvVhTl131W2aDlGSDpVBBTM5kwNyig2N2nCKLDFi3txNmYAliH2+pKuDrNJmMEu/+u7wZMHX4le0dLHNJdry7iIiIiHqxRlUA9qsF4zM4khS0oXcHt6FX5oBl+Tm8K0QDkUwyBJypB2bA+pQYlZ91KciAlQZkwNQJMNGhk5LHAIyIiIgow0SThYcuH4vZY/pmdCwiW5VowsMVciFmkQHLbHllNI4szYAJQRmwUl8GLBWdG0UJYqc3AFO/B7rZGTFlsuPdRURERNSLNXU5AACDK4syPBLVml0JRmDOEFmkZPeZDq3dDry44QiAbJoD5v97cAbMF4CJ456MEu/+RQmiTZX1sqZ5QfB8lh3vLiIiIqJeTMwBU59QZ4pIpCTahl5kkdRBTC60oX9yzX7lZ0OWliCWBCzMre6K2GZxJv18viYc8r7UQVe3nQFYqjAAIyIiIsogj0dCS7ecAasuzYIALMkSRKcrUgli9gZg6nl4qWjpngqBAZg5oARRvTyBPQUZsNKADJh63leX3YUf/2cbnv/scNLP09sxACMiIiLKoFaLQwlMKouNUbbueakqQVRnkURDjmwOwMoKfcFNQ4c9wpbpY9T5H6/AEkQA+N7s0zBhYBmunDQg6edTzwGTJMkvAFvx5Qm8uOEofvrq9qSfp7cL/isSERERUdqI+V8VRYasmHuUbAmieh0w0bZBdOhzZXEA1mH1NZloszgyOBKfoBLEEAHY3RedirsvOjUlz1eqakNvc3r81qf7qr5D+dnjkZRMKcUv8//KiYiIiHqxbJr/BagyYD3Rhj6LA7BWVdB1x4zhGRyJjzoAKzToejxAFxm2unZrUNfD3fWdys+iSyIlhgEYESWMizISESVPtKDPhvlfgC8AS3gOmNIFMXgOWDZnwJq98/Ce/uZkDK8uyfBoZOoALFT5YaqdPawSBp0G2493YOWOBr/7WlVNPtpT0PCjN2MARkQJ+aheg7N+tRpbj7ZleihERDkt2zJgOqUJR/IliEJOZMC8AVg2zMMTjGkOwPqaC3D5RHku2R9W7QYAFATWQQJos2ZHiWauYgBGRAn590EdOmwu/Og/X2Z6KEREOa0xywIwk14+PbQluO6TU2lD7ytBFEFdKtaq6iktWRiA+WfADOE3TKHbz5PLL8XcxOkj+gQFYaloed+bMQAjIiIiyqCmTvlEt09pdpz4V3gDEFGSFy9HyAyY/HO2ZsBcbg+6vPOayouy4+8AAKUG3/FKRwYMAEbWluL806qV3wdWFGJkrdlvmzYrA7BkMAAjoqQYs2SxSiKiXJVtJYhV3gCsJcEALGQJoi6754DZXL7MXJFRl8GR+KtQvSWKjelrXv5tVROS2rJCjOnnH4C1Z0mXyFzFAIyIkqLPgpbJRES5TARg1VkSgFUmG4C5QqwDluVzwOyq9a6MWfS9VqqqOrQ4EysJTcS0EVU4fUAZAOCUmhKM6R+QAWMJYlK4DhgRJUXPdUCIiJKiBGBZ0gWxMskSRKdHzAEL0QUxS+eAiQyYUa/NqvWt1EPptKUv6NFoNPj7TWdi/YFmXDSqBluOtfndn+h7g2TZE+ITUc5QL85p1PNjhIgoUR6PpDQ7yJoSxBKRAbPHtL0kSdjf2AW3R4IkSaoSRHUGLLvngIkMmCmLv9M6belde0t0RNRqNRhVWwqNKhg81mpN61jyTfa+y4goa1lVZRDMgBERJa7N6lSCEhH4ZFpFkQjAYsu4vPT5UVz0+zW47+Ut3iBMvt0vA5btc8CcctBo0mfP/C/hnOGVAIBrzhqUsTEUGfUY2bdU+f1YqyVjY8kHDMCIKG7qq3AaDQMwIqJY/f2jA7jgkQ9xvE3OIIjyw/Iig1/AkklVxXImLtYM2F8+2AcA+O/WOqUFPZBjc8Bc8oXFUGteZdpfrp2If956Nm4+Z2hGx/H7b0zA7ecNAyBnwKQE14kjBmBElIAuuy8DJr60iIgoul+8tQsHm7rxm7e/AgDsru8EAAyuLMrksPxUlviacMR7ku1QzfEKOQfMW6aYam6PBFsSTSrsLpEBy75T49ICPc47tTrjTa/G9i/D9+aMBAB02V1oZyv6hGXfu4yIsp56IrA1wYU6iYh6s0PN3QCAL460AQAmDSrP3GACiDb0TreETnv0eUfqgEq90LK6RF39c08kwa59ej2m/ur9hBtViOCtwJB9JYjZpMCgU5rFcB5Y4hiAEVHc1F/Iom6eiIgiU3cAbOqUy/u2HG0FAEwaXJGRMYVSYNApa2G1dEXvdqeOp1zeEkSjTutXoq5TBWAuT2q/NzweCRsOtaDN4sT6Ay0J7SObM2DZRjSLYSfExPFdRkQxqW+3KVcIu1RzwGwsQSQiikmTKphp7LLD5fagoUMOxIb2Kc7UsEKKpxW9uqJQZMD0Ov/5waILIpD6eWDqUrjCBDNYzIDFrrJYXpyslQFYwnIiAFu7di0uvfRS9O/fHxqNBq+99prf/V1dXbjrrrswcOBAFBYWYsyYMXjyySf9trHZbFi0aBGqqqpQUlKChQsXoqGhIY2vgih3HWm2YOqv3selj30MQK79FmwsQSQiisnJTpvys9Mt4UBTt5J5ybbmD1VxLMYsqXJgDqUFvf/r8c+ApTYAa+zyNQtxJphdYwYsdr4umQzAEpUT77Lu7m5MmDABjz/+eMj7Fy9ejHfeeQf/+te/sGvXLtx7772466678N///lfZ5r777sMbb7yBZcuWYc2aNairq8OCBQvS9RKIctrKnfUAgL0nuwD4d0EUi1cSEVFkItsl7KzrUBoZGbOkA6JQURz7WmDqDJjDFbwGGOA/B8ztTnEA1ukbY6Lzku3MgMVMZEdbLQzAEqXP9ABiMXfuXMydOzfs/Z9++iluuukmzJw5EwBwxx134KmnnsKGDRtw2WWXob29HUuWLMELL7yACy+8EACwdOlSjB49GuvXr8fUqVPD7tvpdMLpzM4uL2Jc2Tq+bMfjFzu32/eFZrHZ0ab60LU6XDyGMeJ7Ljk8fonjsUtMqo9bQ7v/2knbj7cpmRcdPFn196kolE8RGztsUcelbsJxuEnu6tinxBR0/DQaOViz2h0oMaZuCZMTbb7j2mGxJ3QcLXb5MQatJmv+Dtn677asQA5SmzqjvzcyIZPHLdbnzIkALJpzzjkH//3vf3HLLbegf//++PDDD7Fnzx48+uijAIBNmzbB6XRi1qxZymNGjRqFwYMHY926dREDsJUrV6KoKHtaw4ayatWqTA8hp/H4RbfjuAaA/IG7/I13sPO4FiKBbnO68dZbK8DlwGLH91xyePwSx2OXmFQdt00nfJ+lALD2ywNwuOTP0o/WrEapISVPkxJtDfLn/EdbdqNfxy5Eqsyz2XUA5C+BFR9tAqBDkbNdOW7i/1ro4IYGq957H+Wm1I31ozrfcf38i20orN8a9z62HZP30Vh/HCtWHE3d4FIg2/7d1nnfx7v2H8GKFYcyPZywMnHcLJbYFqjOiwDssccewx133IGBAwdCr9dDq9Xib3/7G2bMmAEAqK+vh9FoRHl5ud/j+vbti/r6+oj7njNnDsxmc08NPSlOpxOrVq3C7NmzYTBk0ad2juDxi92OlXuAI4cAAJOmnofNnxwEvP92JGgwa87FMLFsIyq+55LD45c4HrvEpPq4HV17EDi0F6fVlGDPyS4ctxkAyBUG8y6eg9KC7DktO7r2IFaf2IvPGrXoMpThtTunhd32oW0fAk65MkJXNQg4UofzJpyK2ecO9jt+9298D26nBzNmXoCBFYUpG+u2d3YDhw8DAIadOgrzZgyLex+739sHHD2AU4YNwbx5o1M2tmRk679bz7YTWH7oS5jKqjBv3lmZHk6QTB63jo6OmLbLnn/pSXjsscewfv16/Pe//8WQIUOwdu1aLFq0CP379/fLeiXCYDBk1Zs+lFwYYzbj8Yuu1eKb89Vh96Db4T/vyw0dj2Ec+J5LDo9f4njsEpOq4+byVuqNH1SOfY1d6FYtal9SaIIhixpA1Jh9AdKOus6YX/+BJjkDMKpfmfIYcfwMWi1s8ECjTe13RqfqONrdUkL7Fn+bQlP2/RvJtn+31Wa5MqzV4syqcQXKxHGL9flyPgCzWq34yU9+gldffRXz588HAIwfPx5btmzBI488glmzZqG2thYOhwNtbW1+WbCGhgbU1tZmaOREmeXxSFi26SgmDCrHqNrIWV51p6PmbodfG3og8a5TRES9iZjvZS4wYER1idLYSKMJblqRaaLRgiBJEjwS8OSa/Zg2ogpnqNYtU7fU2O99TafUlATtU+d9janugthh9X0nqYPaeCht6LMoCM5WZYVykKE+7hSfnH+XiSYZWm1Au1OdDh7vSeHkyZNhMBjw/vvvK/fv3r0bR44cwbRp4VPqRPnsi6NtuH/5l/jJf74EAOw72Ymv6kOnzptUAdjdL37ht5YN4Ft4k4iIwrN7F643GbSYNLhcud2k91+0OBtUlvgHYHaXB2v3NOJ37+7Gw2/t8rtP3YSjy+6CTqvBkKrgdc1EJ0SXx4Mf/2cbfrliV9A2iei0+xofWJ2JBQW+vw3L6aMp8ZbKqpekofjkRAasq6sL+/btU34/ePAgtmzZgsrKSgwePBjnn38+fvCDH6CwsBBDhgzBmjVr8I9//AN/+MMfAABlZWW49dZbsXjxYlRWVsJsNuPuu+/GtGnTIjbgIMpnHd6FK+vbbXC5PZj1h7UAgO0PXowSk/9HQ2Ab4j3eK5yCixkwIqKoHN6Osia9FmcOqcQrG495f8++k/7KIv8ArNPmwu4GucNhYPvxwEtwQ6uKYNRr4XT6Z6PEWmBfnejEixvkRhf3XzLKb42wRKQiAyaWA+A6YNGJc4Quuwtuj5T03683yokAbOPGjbjggguU3xcvXgwAuOmmm/Dss8/ipZdewo9//GNcf/31aGlpwZAhQ/Dwww/jO9/5jvKYRx99FFqtFgsXLoTdbsfFF1+MJ554Iu2vhShbOL2LZbZbnUpZDCCvbB8YgDV3RV7rgxkwIqLoRJbFqNdiwqBy5fZsPOkPzIB1211KeWHgWltSwFdAqPJDANB7q5U+P9Si3OZweVBolAPQtXsasb2uHXeePyKujGCHzZcBszgSy8qI9S2LTTlxapxR6mYx3Q4XzAXZOw8sW+XEu2zmzJl+6e1AtbW1WLp0acR9FBQU4PHHHw+7mDNRb+P21uB3O9xKMAYgqJ281eGGxftlO35gGbYdaw/aV6rr+TPtZKcNP39tB66bMhgzTqvO9HCIKE+Ii10mvc5vjpUhyxZhBoDSgECky+7CgaZuAFC+E4TAc7RTa0pD7lNkSjYdblVus7vcSgD209e+xNEWKy4YWYPR/WLrQP2TV7/E4WZf6+9EM2ANnTYAQE1pCvvj5ymTXguDTgOnW0KXjQFYIrLvXzwRpYVTFTQ1q+Z4BV51bPaWHxr1Wgwo928bbND56vnzya/f/grv7KjHjc9syPRQiCiPOJQATOuXRcjGz9DA74JOmwsHGr0ZsIDSwsBLcOEzYN4SxPpO5TZxTCRJQkO7/H3THePcIo9HwgufHfG77WBTN74McaEwmpMd8nP3NRfE/djeRqPRKJUynTbOA0sEAzCiXsqt+sJXdzkMLPoQ5YdVxUaUB8wJEJ2Q8q0EsbHTHn0jIqI4qecZFaiaPajLwLPJ/PH9lJ+PtlrQapFL/Rwuj1JFAcReghhqrpB47V12FxzeagyHO7bjcbQ1eNHb+g4bLv3Lx3h89b4QjwjN7ZHQ1CV/7jMDFptSb9arS9UAJZINB1vwj3WHIla09SYMwIh6KXXQ1NzlCzjcAeWEIjirKjGioshXZmDUSsq8hXwrQSwyZt+EeCLKfSLYMAbM+RJzw7LNY9dMwkTvXLXArFK4uVYaDTCiOvYATARb6rnGzhgv6u1WZdIA4IPvnY/ZY/oCAFbuqI9pH/Jz2+GRAK0GqCphABaLeDNg33hqHX7++g58vK+pJ4eVMxiAEfVSrjAliJ6Aq1PiqmBlsQnlqgCsUOebUO2K8Wplrig2+s992HeyCzc+swGbDreEeQQRUXQO1Rwwv9uz9DNUq9Wgr1kOSLYda/O7T92IQ53VKDXplTldgfQh1joTx0T9PeSIMSO4p8E/ABteXYLbzxsOIL7SuJPeqoc+JSZ29IuRKKGNtwTxWKu1J4aTc3KiCQcRpZ46AGtRXXkMlwHrE1CCWKD3Xc3MuwyYyXfy4PZI+O/WOqzd04gB5YWYPKQygyMjolymNOEw+F//DvzczSaiK+DWoAyYKgBT3R6Y3VPTaYPvUwIwVSWGM8aAdE9DV9BtIjDoiCMwaOjwNuAwM/sVq9IE1wLLxoYzmcAAjKiXUmetImXAxH2VxUaUF/oyYAU6VROOPJsDVqTKgHVYncqaaWL+BhFRIpQ5YDl0EhrYDVHwC8BUXwGRTrBDJMCU7J96LnLsAZicARs/sAz3zT5NHq8SgMU2NwnwZcD6lrIBR6yUtcDizIAZQr0JeqHc+QQgopRyhylBDPzeU5pwlJhQrZqcXKiTVBmw7CyfSZRHdWzaVAFYrGUxREShhMuAZbNBlUUhb7c6fSfe6gt3ocoMhXarLygaUiXvV8x/i7cE0en2YL+3K+MT15+BC0bWAPA1h3C4PDFfNBMdEJkBi504zp0xBLrqC77MgMl4FIh6KfUk55bu8E04RBv6qhIjBqu+iHVa3xdtvmXA1Fdf2ywOpZSFARgRJSPcHLBsduWkAX7zoszeDJPVEfrz0BCizFA43uab/yPWjnK45SAp3iYch5u74XRLKDbq/JZIKVFl7GKdnyTWAKtmBixmJWIOWAwliDYXA7BAPApEvZS6DX1zDHPAqoqNfguHWlwaXxOOPMuAqSfEt1mdSilLrGUxREShBHZBDFz4PhtVlZgwYWCZ8vswb4dDdRfEWDNgNlW3R3EMfE044psDtrtezn6d2rfUb80ynTb+Nap8a4AxAxareEoQ7ap14yK9P3oTBmBEvZT6CqNfCWLgHDBVCaL6S67b6VtUM9+acDhcvtejngOWrZ3KiCg3iBNRsYTHC7dNRV+zCX+78cxMDiuqX1xxOgoMWlw0qgbF3g6HYjFmSZL8vk/0ETJgglbjOwYiKI13DpiY/zWyb2nQfb4OfdHL41q6HTjRLmfmapgBi5k5ji6I6gwY1wGTsQkHUS+lznS1dIfOgEmS5CtBLPZfhNmkUwVgeVaCqA60HC6P8gXjdOXX6ySi9BKfLaIEcdqIKnz2k1mZHFJMxvQ346MfXojSAj3uemEzAF8TjsALcIYIXRCFU2pKgjJgTapKjFgudokA7NS+wWuOmQsMONFuixocHGrqxsxHPlR+5yLMsSuJowuiTZUB43VMGTNgRL2U+ktTHXSpS0ksDrdSMlJVIgdgS28+C2P7l+LaEW7fHLA8y4A5VVfrHG6PrwsivzmIKEFujy9TFKlVe7aqLjWhwKBDobdLbLf3xDswW2WIsI7WbxeOx8CKQjx27RkweucC+bogqkoQY7jYdbCpGwAwoiY4AFM6IVojZ8Be31Ln93tfMzNgsSoxeZtwxB2A5df5QqKYASPqpcItnuzXHdF7RbLAoFVas18wqgbnjqjAihUroLfn50LM6quvVodb+YJhEw4iStT/e3On8rMpBwMwobJIPvEWlROBwVKkhYy/cdYgfOOsQQB8Qajd6ZGrLbriK0EUJZCiFE4t1kWCy4sMfr/3KTGG2ZICxVPmqZ77F7jUTW+Vu58ARJSUcFkrj197elF+GLosI18XYlZ/+bda4l+bhogo0Hu7GpSfCwy50wUxUI03S9ToXTvL7vZv9R5rdk8pQXR70GF1+X2PxPJZK9rXh+ooKVqkR1sLzFzoC96MOi307NAXs7iacLiYAQvEdxpRLxWuc6G6CYevAUfoq4K+hZjzKzCxqzJdTZ3xrU1DRBTI5nSjztuC/eU7pkbMEmU7sR6kWLw4MMukj/G1mVRzwNQdEIHY5oDZvCf1BSHWVIs1A6aBRv0LxUEsIxDLHDA7M2BBGIAR9VLhrkKFas4R2IBD6A0ZsKau+FojExEF2tvQBY8EVBYbcfawykwPJyk1AQFY4DyrWNd5EpkrOQBz+N0Xy8WuWDJg0QIw9fPkcEycEaIJh8XhjnoRlnPAgjEAI+qlwi10qb461eS9KlkZpgRRlGvkWwCm/lJWB2DMgBFRInad6AAAjO7nv2ZVLhIZsMYwGbBYAzB1CaJ6/hcQ/WKXJElKBswUIgMmSgujlSCqM23aHP+7pJt6wetuuzvClr5sJcAATGAARtRLhc+A+X5u8X4phpuY7GtDn1+BiX8GjCWIRJScnd4AbFStOcMjSZ5YK6u52w6X2xMU5MRaXql0QQxRghjuAqH6fnGtMHIGLEoApvpMry1jB8R4GPVapYy00x75OCfbhONgUzc+O9Ac9+OyGQMwol4q3BVGvy6I3ZHngOXvQsy+Y9OozoDlWaBJROnxVb3IgOV+AFZVbIRGA0gS0GpxosOaXAbM7nIrF/uEaJ+16qYOoeaAxbpIsPp78M/XTIo8YAoS61y7ZNcBu+CRD3H10+ux72Rn/A/OUgzAiHqpWOaAiQAsbAliDy7EbHO6sf5Ac0aya+qrr46ANcEkTiAmojhIkoRdJ+QTx9H9SjM8muRptRoUeLNONqc7KANWZIytw6MvAPPNAavwtoV3Rqk2EBkVjcaXSVOLNTAQAdi1Zw/CuAFlMY2bfJROiFEacagbW7mT+A7dUdeR8GOzDQMwol4qXImH+sOxqVO0oQ+TAevBOWD3vPQFrnl6Pf78wb6U7zsae5gvf0nKv2wfEfWsE+02tFud0Gs1OCXEosG5SGSd7C63XxOOYX2K8d2LTo1pH6YQAZhYCDnaHDCRATPptSHn1MVbghhr1o78ieMcrRW9OgPmifM7VJ3tzKcOinzHEfVSbm8b+sB6ffWH47FWCwBgQEVhyH305Bywd3fIa+Y8+8nBlO87mkhf/uyESETxEOWHI6pLQs5XykUmJQPmUbJM373wFKz+/kylSUc0hd610OxON5q9pd79ykQAJn8P/WHlbvzw31uDKg9sETogArFnwBze52EAlhiRAYvW7EQ9ByzeJhzq4K4nqm0yhe84ol5KZHIqA7Jb4sOx3eJEh/eDb1BFUch9pGMOWCYWLI3UbIONOIgoHvlUfij4ZcC8J9/mQkNc+yj0lipanW5lyZPaMvlin8Pb3OPPH+zDKxuPob7D5vdYe4Q1wADfGlUdNmfEsnHxeR7r4tHkT7Sij1aC6JcBizOLpd53LGuO5YqUvuM6Ojpw8uRJuN2R21ESUeaJK0mB5YWiBPFIi5z9qi41KV+UgfRiIeYwizrHyuZ0Y/6fP8IP/7016L5MBGCRslxsxEFE8VA6IOZBAw5BnQETJYgi6ImVyIAdbbEq3ze1qhLEHcd9830CL3zFmgFzuqWwJeXieQBmwBIljnO0EkR7Em3o1VnMdmvkTFsuSfgdd+jQITz11FO47rrrMGzYMBQUFKCiogL9+vWD0WhEZWUlpk+fjp/85Cd49913GZQRZRnxIRjY4VCUIB5u6QYADKkMnf0CAJ3WOwcsybKA1V+dxI66Dryy8VjQfYVpDsA8HiliRo8ZMCKKx1cn8qcDoqDOgB32Bk815thKDwVxYe9IiwUWh3yO6CtB9GD78XZl28A5y9EyYCUmvRIc7DvZFXYM4vPcxAxYQkpNsXZBTLwJh3rfgR03c5k++iY+Ho8Hr732Gp566im8//77kCQpbGq3ra0N69atw/r16/Gb3/wGNTU1uOWWW3D77bdj6NChqRg7ESXB6c1aBXY4FB+OdW1WAMDAMPO/gNAliJ8daMa6A82464JTlCYd0UQqKwj3BdtT1AtGhsIAjIhiZXO6cbBJvpiVTyWIIvPU0u1UXt+Y/vEFmKG6JYogzumS8KUqAAussrBHyYBpNBpMHFSOj/Y24YsjrWE7HPoyYFyEORFKE46oXRATb8KhbqSSTxmwmAOw119/HT/60Y+wZ88eJegaMWIEpkyZgkmTJqFPnz6orKxEYWEhWlpa0NLSgoMHD+Kzzz7Dpk2b0NDQgF//+tf43e9+h9tvvx0PPPAAqqure+yFEVFkSgasOHQGTKxsL2q8Q1FKEFVleVc/vR4AcEpNCb42vn9MY1HXh0uS5NfVypTmDNjmw20R74+2QCgRkbC7vhMeSf6crS6JL0OUzUzeC2Nbj7ZBkuRSdbFAc6xClZcXezMqjsAMmCu+DBgATB5SgY/2NmHzkTZ8c1robURJeahW9hRdSczrgKmbcMT3HOrgrtcFYDNnzsRHH30ESZIwYcIE3HDDDbjuuuvQr1+/mJ7E4/Hg/fffx7/+9S+89tpr+Otf/4rnn38e//znP3HppZcm9QKIKDHOcHPAvAGYqJsviNC1S2TAnErQ5vug7I5jsqxVFYDZXR6/evx0lyCu3n0SADC4skiZl6DGDBgRxUpUEgztUxyyXXquEpmnzUdaAQBjEiivDPXZLrJiDR02pSwR8FVsCHZX5AwYILfEB4CTnbaw2yht6FmCmJASpQQxWhdE1RywpEoQ8ycAi+kdt3btWsyZMwfr1q3DF198ge9973sxB18AoNVqMXv2bDz33HM4ceIEfvWrX8FoNOKLL75IeOBElDhJkpQTg6qSwBJE+f/KOisRrjCKAMztfZBotwz4ShNiYXX4L3asntCb7hJEEYBdPLav3+3ixIBNOIgonL+tPYC7X/xCOeEUJc2xLk6cK8TnslgYd2yc5YcAUGQMzgGUmuTvDXXwBQTPMxbHN9LcLWWdMSebcPSU0kS6IMbbhj5PM2AxvePWrVuHt99+G1OmTEn6CYuKinD//ffj4MGDWLhwYdL7I6L4/WPdYeWDLLANvScgAxbpCqNvIWZ5253edstAfOtlWRy+D1iHy+O3pogG6btqfLi5Gwcau6HXanDhKP8ATDQrYQaMiEL5cPdJPLxiF97YWocPvpIv5ETr1perAl/P2P6h51hFEioDFq7kPXCtSaVCI0KFhChfjzSvV1xQYxOOxMQegCXehKMjT+eAxfSOS0XgFai4uBhjx45N+X6JKLr/++8O5efAAEw01IjlCmNgE44271ouACK2/g2k/lC1uzx+JQfpXPh4tfek6cyhFX7dIY06LUq8V2aZASOiUNYfaFF+/mRfEwDf52i6M/k9LfD1xNuAA0DQ8iYXjapBsSl0QBX4uZuyDJiLCzEnw6jzVoZE+b5PpgmHTZUN7XUBGBHll4oiX3lggUGr1HEDvkUS7TG05xUZMPHhq/6SjDVw2neyEy99flT53eHy+F1NS2fAs3p3IwDgwlE1fpOyzYV6ZaFOJzNgRBSCuszqYyUAi56pyUXqDFixURdxuZJwAjsP/uHqiTDpdSEXRQ4sQRRNosKtUQn4jnmki4F2NuFIii5EJ+RQ/JtwxBeAqTNmVqc7b6pQ+I4j6oWGVBUrP2s1GpQV+gIypQlHDCcOIjgTH4jqL7pYPySXfnLI73e7y+13IpPsGmOxcnskrD/QDACYObLGb1K2ucAAkwg2mQFLOUmS8urKJvVO6s+tw80WHG2x9IoM2Oh+Zmi18ZeKq5uSzB1Xq3wPqS8IinXBAtvQi8+L8qLwc43F95P67xLIySYcSRGdkKMFVX4LMcdZghj4lZsv3xU98o6TJAmffvopXn75ZXz44YdwOBzRH0REadO/3Ncu2O5ywxwqAIuhCYe4UimCEnXQFWsGLHAugcPlSWg/yTrRboXd5YFRp8WI6hKUGP2zgkoGjAFYyv3u3d2Y8OBKfLq/KdNDIUpY4In+J/ualPlHkbrJ5iL153YiDTgC6VQBnPoztn+5vA6lI+BCnDgJV188DBRLBoxt6JOjdEKO8r2ozoDFW4IYuH2vDcCam5vxwQcf4KOPPgp5/8GDBzF+/Hicd955uO6663DRRRdh2LBheO+995IeLBGlhvpi4sRBFSgrDFGCGMPk8cAae/VVrlgzYNaAkxa7y5NQKWOyjrbIXSEHVBRCp9WgTHVl9VCzRSmXiWduG8XmiQ/3AwDue3lLZgdClARxktnH21n2431NMVUS5CJ1BiyR+V+B9KoAzKqa8yO6RwY24VAyYIX+c5jVYsqAiQBMnz9LBKSTXisf42gZsGTa0Adu32sDsL///e+YPXs2/vCHPwTd53a7cfnll2PHjh2QJAnl5eUAgBMnTuDyyy/Hnj17kh4wESVPlHP8ZuHp0Gk1qFYtoBmUAYtQmmEMKMtTT3YOvGIZTmD3pOAMWHpKEI961/wapJrLIK7s9ikx+rJ9DMB6TEOHPdNDIEqYyHbNGl0DAPh0f7PS4TXfuuypX8/I2lRkwHz7U88nEs0xAkvR2yxyZZU5xgyYFOakXylBZAYsIaIEMdIcMJfb43d/vNdUAzNg+bIWWNzvuDVr1gAAbrnllqD7li1bhu3bt6OkpAQffvghmpubcezYMZx55pmw2Wz4/e9/n/yIiShpIjgSV6/uuegU5T53QBOOuOaAqT5ZYw1UAhdwzFQJ4tFWbwBWUajc9uy3zsaCSQOw5KazlC9oliD2rHgnaBNlC5G5mTq8CoUGHVq6HdhdLy/NkW8ZsG5Vluq0viVJ7++soRUhb1dK3MLMAYtUgqgOEsNVLigliHkWIKeL0gk5wvdi4LGPtwSRGTCvvXv3QqPRYObMmUH3LVu2DBqNBvfeey9mzJgBAOjXrx+WLFkCSZLwwQcfJD1gIkpe4MTjU2pK8e0ZwwGEWgcs+hwwkS1TZ8BiDVTULefFvuwZKEE83uYrQRSqS034w9UTMWFQOTNgPcisWvun2xF5PRmibGXzfjaUmPRKp9mmLjlTk29NOOrbbcrPoRZUjtWq+2bg4SvH4aozB4W8X7nw5QoMwOTPiUhNONRBb9gAjBmwpMTSBTGwBDT+JhwMwAAA9fX1MJvNKC0tDbrvk08+AYCgBZZPP/10DB06FMePH09wmESUSqIE0aCquxddrES841tnJYY5YCHa0McaqHQFBWCZKUEUr6E4zMmEiU04eoz65CjSmj1E2SSwrM2udDzUKYsAi5NFU55lwG6YOgRGvRY3ThuS1H5O7VuK66cM8WvCMayP3KV34qByZe6t+gTf45HQbpUD20gZML1WA7Fbe5h5YGzCkZxwJaJqtiQzYJ7enAHTarXQ6XTQ6XSwWCxob29Xflf/19jYCEmScMYZZwTdd+jQIdjtdr/bHnrooZ5+fUQUgihBVF/103lbAgetAxZDF0SxrfpLLvYMmPxhKhaEDg7A0nNCHm0ugEHHDFhPcLo9/gGYK/yEeaJsYXO6cdlfPsH1f1+vBGLqlvPigo2Y45pvJYgja0ux5eez8eBlY1O+76U3n4VvTR+Kv95whrLWpLgQ98WRVpzy0xXK75EyYBqNJmonRLEfliAmRqeN3oY+VRkwsTxBvgRgMeWNV69eDUC+2jNnzhwUFBTgjTfe8NtmzZo1eOCBBzBx4kQ8+uijfvdJkoTFixdj+/btWLVqlXL70KFDkxw+ESVC1Gur1z7RKqUE/sFUpPbJ6jlgkiQllAETJYhVxUa0dDsyNgdMPE/g4qCCuEJqZwYspbrtwRlQomz0+pbj2Hy4FVOGV+FAYxe+PN4OAOi0u2AuMCgdXdUZMKEgD0/wkyk9jGRon2L836VyYCc+j8Xn8+JXtkKc6xt0GhRGCWxNei0sDnfITohuj6Sc3LMEMTG+JhzhP7frvOX9QtwLMXt3XVFsQJfdlTdNOGL613P++ecrP9fU1ODEiROoqqrCuHHjlNuXL18OjUaDiy66yG97we12o7y8POR9RJReSrChKvsQGTDxYRdTBkz1peVwewK6IEY/kfZ4JHR55/z0KTFh78kubxt6dSYtPSWIrihfxCJYdbrYJCKVAucARmoZTZRJ//vqdnTaXXhu3WG/29stTpgLDEob+gKDLmjubL5lwNJFNIoSFw3VJ99lhQa/xZxDkY+7EzanB4+9vxc1ZhOuPmswAP+Le8yAJUb8fTyS/H0eakHujYda/X4PLCmMRmxfWWTE0RZr3mTA4n7HTZ8+HZIk4Tvf+Q6am5sBAJ9++imWLFkCAFiwYEHQY6xWK3bt2oVTTjkl6D4iSj9Rr63OgIm4w+OR/NrGRmrCob7PEbB+VywZsG6HC+KzuKrEqDxOHXTFEsilQrTJ2L6W+wwQUim4CQszYJSdLGEuDrR6W6KHKkEUGIAlRmnC4f0+Uq8bGWn+lyD+DusPNOP3q/bg/uVfKvepv1vCVT5QZOq5e/6t5n3ZxY2HWwAAo2pLlfsAuWvoz1/fjk/3NUV8DrF9hXeaQq8NwBYvXgydTod169ahtrYWNTU1OO+882C1WnH++edj2rRpQY95/fXX4XK5cO6556Zk0ESUHPHFow/VhEOS/E6CIzXhUAcrdpfHb/5OLKWD4uRbr9Uo67kEliBGam+bSmK8+nAliMyA9YjfvPOV3+9swkHZKlzpVJvFCY9H8lu6I/BzM9+6IKaL0oTD+/kcfwAm/x0+O9is3Cb+jurvGTbhSIw6cBVliA6XB3MeXYMFf/0ULrcHXxxpAyAvzwD4qmyeXnsA/1h3GNf9/bOIz6HOgAG9OACbMmUKnnvuOZjNZrjdbjQ1NUGSJJx55pl44YUXQj7m0UcfhUajCeqOSESZ4Zvv5PsIEMGY+kQCiJwB02o1vsxQQOC0encjPjvQHO6hAHwBWGmB3jfHyuX2e36PlJ61ocTVu3BfxIGLTlPymrrsWLOn0e82G5twUBZSd24L/IxotTj8PrMKDbqggIsZsMTolTlg8vFXV6/FEoCJv8OOug7lNpGpVM/7jVbKSKGFyoBtPtKK/Y3d2Hq0DUs/OQSLw43SAj1GejNgIqA65l17M5rADFivmgMW6LrrrsNll12Gjz/+GK2trRg+fDimTJkSctuOjg78z//8D/R6fdhtiCi9XCE6P2k16gyY/AVl0GlC1nSrGfVaef6XyxNUPnb10+tx6Nfzwz5WdEAsLTAoc80CAzlA/qLUaXv2BCZqCSLXAUu5wLkBADNglJ3UndvMhXplfS9AviKvnrsYMgMWoZKAwlNKEN2eoPblJQWxZ8BOqNYtszndKDbplc9yZr8SZ9D6jp3be15xpNkXWD28YhcAYPKQCuUirwio9DEed7F9ZZ6VICbcwqakpASXXHJJ1O3MZjNuuummRJ+GiHpAqBJEdTtZcRIcqfxQMOm16LKHDpyi6bT7MmBFBvnjqNPmCsoyOd0e7Khrx/LNx3H/xaNQFqH1cKKilSAamAFLuY2H5LkBRr0W1SUmHG+zsg09ZSX1ub+5wOAXgLV2O5XMrUGngU6rCWpeVFrQMx0D8516nakWi8PvvlimbZWEOO7WwAwYG3AkTKvVQKORM5NObwnivsauoO3OGlqpnGOIDJgxxnl3YvsKbwlit8MNp9uT850rc3v0RJQQV6h1wFQfjg5VaUY06sxQvA0URAliiUmPoX2KAAD7G7vgCDgJd7ol3P6PTXjhsyO4/Z8b43qOWEXrgsgMWOp9fljOgP3u6+NxWt8SAGzCQdlJnX0pMvlfmGqzOnwdEL0XrdSl2xoNlDmuFB9xkdDp9qChw+Z3Xyxlg4suGIEbpg7G8jvPUUoWxd/KHqXqgWITmNnaXd8ZtM3kIRVBa4bFetzF9uo13/KhDJHvOqJeKNQcMKUE0SP5SjNiuDIoTjQsDlfcc7XUJYin1sj14XsaOkOWILZ0y1c/NxxsURY+TSVnlHIUpQkHM2ApYXG4sMO7jtKZQyuVbKudbegpC6lLEHVa/8+Ixk47mrrsAKBk59VzvsoKDX5zZSh26i6IJzvtfvfFckQnD6nEL644HZOHVCjzwXxzwCLP+6XY+JYKkI/n0RBzuyYMLPc7xwD8M4+Rzh1EU2SDTosio/zvyuLI/e+JmN51n3/+ecqfWLSmJ6L0kiRJle0JVYLoK7OLJQAT2wS2ExcirevU5X2MuUCP4dXF0GqADpsLx1r9F250uDxKC1vAvxNWqjjcoi493ELMGmUslLwtR9vg8kjoX1aAAeWFyskRM2CUjdQZMH1AMLVqZwM+95bTDqmSM/nqDJgonaL4qbsgngzIgMUUgamIoDiwCQfXAEuO+Pfg8kjweKSg7+8fzx2FQqMuqARRfQE40ne6+Len0/oekw/fEzG966ZMmYLLLrsMmzdvTvoJrVYrHnnkEQwbNgzLli1Len9EFB/1GlvqSbBiIWaPJMU1OVlkLgKvTgqBZSNq6i6IBQYdRlTLZWiBNeQuj+T3Yd0TreBFC92oJYjMgKWEaMBx5tBKAL73UT58sVL+UV+g16lK384YXA67y4PfvrMbADC4shiA//zZ8h6Ys9pb6JUmHBIaOvy/Yy6b0D+ufRUqAZivXTrADFiyxEVLt8eDpi570EXKBWcMBOCrsvn8UCu2H2+H+jqGxR76Aq68X0l5fD5VosT0rps+fTrefPNNnHXWWZg6dSr+8pe/oLGxMfoDvSRJwvvvv49bbrkF/fr1w/333w+LxYKJEycmOm4iSpD6g0v9xWPQy5+Gdpc7ZIliND959cuQt9e3RwrA5BJEMVF6zti+APxbDQeOGeiZIChaCaI4FtuOtWPXiY6Q21DsRMbgrKEVAKA0LYiUMSXKFHUJoroCcfHskX7bMQOWWuouiOJi3s3nDMVri6Zj5siauPZlCsiAKXOd9SwPTYYoyXW6pZDlh6JsUF2Ge9MzG5SSRUBurBGOyJjpApa9yXUxteX56KOPsHz5ctx///3YsGEDPv/8c9x777047bTTcPbZZ2PChAmorq5GZWUlTCYTWltb0dLSggMHDmDDhg3YuHEjuru7IUkSdDodbr/9djz00EOoqYnvHw8RJU/9oacuQSz3niS0WZzKh1ukNcCEL73zeMT2l07oj6MtFnx2UD7BbgiTGQPUGTD5CvHlEwfg8dX7g7ZzuDx+QVhPXP1yRi1B9B2L/31tO5bfeU7Kx9CbiPfNpMFyACbKg5gBo2wkyqA0GkCjqn2bfkoVzh5WiQ3ez7shlXIApp4DxgxY4pQSRI9HqbI4paYEEweVx72vAu/3mSh3YwYsNdRNOALLDwFf5lF9mJu7HX7f490xZMB0eZYBi7kv6sKFC3H55Zfj3//+N5588kmsXbsWX331FXbv3o1//vOfYR8nJstXV1fjW9/6Fr797W9j2LBhyY+ciBKizh6pr0iJq7St3Y64mnB848yBeGXjMdxz0am4d9apSmeq7774Bf67tQ4NkTJgqjb0AHBa31KMqi3FVwFdlFwe37w1IPUfvpIkKS10w2X91OWarQHtkCl+YhK1WNtFBPtswkHZSGTAtAGd9zQaDR64dCzuX74N5UUGnHdaNQBmwFJFyYC5JJy0yd8lfc0FCe2r0Bh6Dhi7ICbHt1i2B0dbgjNgYi3RwH876ottkZpqKP/2emMGTNlYr8c111yDa665Bnv37sW7776LtWvX4rPPPsOJEyfgcvkiWLPZjDFjxmDGjBmYOXMmLrroIhgMvApElGlirpNRp/Vr41spAjCLU9WGPvoX00/njcHCMwbi7GGVfvvrazYBAOojzgHzliCafB9Fl03sj6+88ykEpzswA5baOWBuj6SUPYa7GqqeeD+61pzS5+9tJEkKOvlRArA8+GKl/ONRXYUP7H4+pr8Zb9x9rt9t6nXAxEUGip8oX+t2uNDsXXtNfLfESywRYHMFzAFjE46kRMuACYGdQI+ogrVuR/gMmK8Jh8Y3VaI3ZcACnXrqqTj11FNx1113Kbe1tbXBZrOhqqqKwRZRlhINLAJL7cqL5X+zVqcbHd7SwFi+mMqKDJgyvCrodnGVMpYmHOYC3+fFpeP7KxPafWP2KHO0gNRnwPwbk4QuQRw/sAyVxUa0BJROUPxCBbyiaQHngFE2Egl4rRb44SWjcMXjn+C2c8NX86gv2EwZVtnTw8tbojqi3epEo7fVf01pYhkwpdNqYBdEZsCSIqpDXJ7Qc8CEwAzYtmO+6QsWewwZMI0vA+bMgwt1KV2avby8PJW7I6IeEC67VWrSQ6/VwOWRlHa/yXwx1ZZFD8C6AkoQAWBQZRFumjYEu050os3qwJ6GLjjcHjh7sARRlB8C4bN+Go0G/zt/NBa/srVH2uD3JuoyWHFF07dGT+5/sVL+ESeBOo0GEweVY8eDF6PYFNsplJjnSPET84NFZkWjAfqUJJZRFPPyrA7RhCO+BYEpNKUNvTtyBkxUvAhi7TwgWgZM/r9Oq1H+VvnQjTilARgRZb9w7dY1Gg3Ki4xo6rIrQVMypRkiAxa5BNG/CYfw4OXjAABXPP6JPGa31KNNONRX0wxhMmCArxzGmgeLQGaSehkB8T4sMspfR5G+iIkyRZRBifks0YKvmSNrcO3Zg3DuKdVchDkJJQHHuU+JyW8+bjyUdcBcAU04WIKYFPH+drjdqGuTA7Aioy5oXldLtzPoscKJtvDnCb2+CQcR5Qdx8hsq0KgsNngDMPnKVFIZMKUE0Q5JkvzmhwHyPKDANvSBDKrJverujY4UrwOmdEDUaoLGqSa+wCNNGKboxNVLjcZ39dRcKAfhHdbwX9JEmaJeiygWBQYdfrVgfE8OqVcI/G5IdP4XoF6IWf78YROO1BAB8fFWK5xuCXqtBjWlJhxq9i9HvHBU+M7nGw+3hL3P14QDedWEg+86ol5GlNuFmuskWtGLtbuSuTJY4/2idLg8aLMEn1TbXR4l8CkNG4D5yg0cPZkBi/GLWGRpWIKYHPXxFgFvmQjAbMyAUfZRr0VE6VNi9P9uqE2wAyLgK3MOakPPdcCSIi6iiYCrf3mh3zIMQm1ZAbb8fDY+vv+CoPs2H26FK8z3uroJhzgnYQBGRDlHXMk1aIP/+Q+qkNewOdDUBSC5AMyk1yndv0KVIXaqTrQDv2QF3yKckt+Hc08FYOEacAgsQUyNUJPfzYW+yfZE2UZpwhFjBoxSQ6vV+JUhjqgpSXhfylqDARkwNuFIjhKANXUDAAZVFuKRqyagT4kJv1l4ut+25UVGDCgvDFobr9vhDlp+RlDPv1QCsBR3Qs4EvuuIehlRyqcNcSV3dL9SAL6SvGS/mGpKw7eiV7egDzUWwFeCaHe5oerB0WNdEKO9Xl8JIrM0yfBlwHx/9zJVCaJYP5IoWyjzUHjWlHbqAOzUmtKE96M0i/D4t6FnCWJyxIXLg81yADawvAjjBpTh859ehKvPGhy0vUajwaja4L/j54dClyG6VfMvDSxBJKJcJT7M9CGCnjH9/Ne3MiQ5OVl0QjwZIQMWrvwQ8H0xBraoTfXVr9hLEL0ZMJYgJsXhCu4+JgIwl0fiHDvKOh7VVXhKL/U8sFOTyICJzxtxEVIsecEmHMnReatpRAZsYEUhAEScTz2sj+/vOGFQOQBg46HWkNt6VOcsLEEkopzljjCXYXRAAJZsBkzU69e324PuiysACzghT/UaIPGWIDoDujJSfEIFvIUGnXJRgGWIlG3cnvCVA9SzWrodys+nJBGA6VVNnQDggFIyV5TE6Mjg/TchqlRqYmiUUqEqQTxnhLyO6OeHWkJWP4RcBywPvn8ZgBH1Mm7RhCPEiURFsdFvknOyVwZrIrSi77LLJ9mBLejVlADM6V/yl+kSREDOgp3ssOH5zw6zJDFOytwL1ftLo9GoGnEwAKPswjlgmaMOwGJdey0UMe/Z5f1jijlHI0OUw1HsAi/mxvI3qijyreV21tAKGHVanOy040hL8ELO6nXAfHPAGIClxdq1a3HppZeif//+0Gg0eO211/zu12g0If/73e9+p2zT0tKC66+/HmazGeXl5bj11lvR1dWV5ldClHmR5oABvnlgQOoyYKFKEEW3u8B1XtTEHKHAphepDsBcMZYgmvRaiMNmdbjx9SfX4aevbscvV+xK6XjynSPEHDDA14q+PUTXTKJMYhfEzPnm1CEAgP+dPzqp/agzYC3dDjR2ypUZp/VlAJaMwO/N4jBNtdTUTThKTAacPrAMAPB5iDJEddUO29CrOBwOHDhwABs3bsTGjRtx8OBBOByO6A+MQ3d3NyZMmIDHH3885P0nTpzw+++ZZ56BRqPBwoULlW2uv/567NixA6tWrcKbb76JtWvX4o477kjpOIlyQaQ5YIB/GWKyGbDasvBNOOzeD1DRGjgU8cHe3cNzwBwxliBqNBpfK3qHW7la98GukykdT74TGcfAL24zW9FTlvKtA5bhgfRC988dhf/8zzm47bzhSe3HoCpf23dSvgA/sKIw4kVAii6RDJjokAzIF+LOHFoBANgYohGHeg0+9dI0uS6hd53H48Fzzz2H5557Dp999llQwGUymTB16lR861vfwvXXXw9tiHbX8Zg7dy7mzp0b9v7a2lq/319//XVccMEFGD5c/se6a9cuvPPOO/j8889x5plnAgAee+wxzJs3D4888gj69++f1PiIckmkOWBAagOwvspizMEBmBTDFWXxYWvt4RJEkWErDLF2SaACgw5ddpffvDRdlMCN/DnDdB8TJYjtVidOtFvxxOr9uOmcoUnN+yBKBWbAMqfEpMcZgyuS3o/IuLvcEo61yhfPBnP+V9ICL1wWm6J/j5YXqQMwLc4aUomncCCoE6JH1f4439YBizsAO3LkCObPn4+dO3cCQMgJczabDWvWrMGaNWvw+9//Hm+++SYGDhyY/Ghj0NDQgLfeegvPPfecctu6detQXl6uBF8AMGvWLGi1Wnz22We48sorw+7P6XTC6czOchgxrmwdX7brrcfP7p2vpNWEfu2nVvu+kHSQQm4T67GrKpQ/iJu6HOi22v0COqfLG8BIoZ9DHqP8+dIdkBGxOVwp/bu1dssBYolJF3W/xUYdmgC0dfuCSp1GE9N4eut7LpDVIb9+vdb/WJR6m5y0dNnw7X9uxLZjHXhzWx02/FheuJPHL3E8dokRx8shPjfBYxiPrHrfSd728243DnvXuuxfVpAdYwsjq45fGKUBAZdJG328pUZf0KaVPBjXrxgAsL+xG11WO0z64GYbHrcLOu85gd0Z+Rwgk8ct1ueMKwBrbm7GOeecgxMnTkCSJPTp0wcXXnghJkyYgMrKSgDyXKutW7figw8+QFNTE7Zt24bp06dj69atKC8vj/uFxOu5555DaWkpFixYoNxWX1+Pmpoav+30ej0qKytRX18fcX8rV65EUVF2XyFZtWpVpoeQ03rb8dvcqAGgQ0tTE1asWBF0v0cCDFodnB4Ndny5FaYTW8LuK9qx80iATqODW9LglTfeQaWqOdL2E/I46k+cwIoVx0M+/sgRLQAtjtU3AvB9YO/avQcrLF9FfO54fFYnj6Wz5WTIY6LmsesAaLD6k/UAvG3pLd1RH6fW295zgTY1yce7o63F77i1Ncp/781f7sK2Y/IXcKvFGXRse/vxSwaPXWI2btoMQIfOzs64/q2TLBved7tavd99re34bHsbAC26Tx7BihWHMzyy6LLh+IXTdEw+rsK6jz7EDmPYzQEA3U5AhCAffbQWNYWABjpI0OA/b76DMu/j5TWz5e3eX7UKe7zfHUeO1WHFimNRx5aJ42axBDcSCSWuAOyee+5BXV0dDAYDfvnLX+Kuu+6CyRS63aTdbsdjjz2Gn/70pzh27Bjuu+8+LF26NJ6nS8gzzzyD66+/HgUFBdE3jsGcOXNgNpujb5gBTqcTq1atwuzZs2EwhO8kR6H11uPXvek4sG8HavvWYN68M0Ju86+6Ddh0pA3nn3MWzjulT9D98Ry7X27/EI1dDpwx9Vy/dcYaPj2MVw/txoAB/TFv3viQj92/ej9WHt+PglIz0Nmp3D502AjMm3NqLC83Jnve3wccPoDRI4Zg3rzIE71fbtiIowdaMHLcROCrLwEAZaWlmDfvnKjP01vfc4FsXxwH9u5Avxr/9+DOlXvxScNB1AwcChw7otw+b948ADx+yeCxi89nB1tw7yvbcEp1MYZpmzD9zInAV1+iorwM8+ZNzfTwckY2ve/K9zfjya82obC4BNoSE3CyBRecPQHzJmXvNJRsOn7hdG08hreO7lR+v2zeHGWudDhuj4SfbJSDo+nnnoeRtaV4aNtqtFqcePZwGX67cBzG9CvF71buBSAHyJdcMgeubfX498GdqKrui3nzJoXdfyaPW0dHR0zbxRyA1dXV4eWXX4ZGo8Hzzz+Pr3/96xG3N5lM+P73v4/BgwfjmmuuwfPPP49f/epXQfO1Uumjjz7C7t278fLLL/vdXltbi5Mn/SfJu1wutLS0RB2PwWDI2je9kAtjzGa97fhpvHMyDXpd2Nf9q4Xj8dmBZpx3Wt+InQFjOXZGvffKmMb/+TQaeb96XfhxFIiGF07/em+3hJT+zbod8v7LioxR92suNAaNSa/TxjWe3vaeC+Tx9n8yGvz/9hUl8gW9roCul4HHqrcfv2Tw2MVm9Z5mNHU50NTlwHrocEiSs/S6OP+tkywb3ncFRvn53R7geLtcQj6kT0nGxxWLbDh+4dSU+arEtBrAXFQQcRFmADAAuPvCU3C8zYqxAyug0WhQUWREq8WJA03d+PpTn+GW6cOw9FNfdtJkNKLQJB8Dp0eK6Xhk4rjF+nwxz7B/+eWX4Xa7MXfu3KjBl9o3vvENzJ07F263OygwSrUlS5Zg8uTJmDBhgt/t06ZNQ1tbGzZt2qTc9sEHH8Dj8WDKlCk9OiaibOOK0gURkNvyfnPa0Kht2WNhDFHLDfgmtUf6nBbrtvR0G/oO78K/5ghrkgli4Wh1p75o3RPJn5hAHbjMgbIOGBdipgwTi4GLxg1i0V7+U89dBu93kd3lQV2bFQAwoKIwk0PKC+qOhsVGfdTgS/jenJH4wzcmKtuXFfl//z7zyUG/3/OtCUfMZ1cbNmyARqPBbbfdFveT3HbbbZAkCZ999lncjwWArq4ubNmyBVu2bAEAHDx4EFu2bMGRI74SlY6ODixbtizk+EaPHo1LLrkEt99+OzZs2IBPPvkEd911F6655hp2QKRex+0NXsKtA5Zq4gQm8AMzloVNxWO77f5NOFLdhl4EU6INeiRi4ehOVQDGxVlj5/ZIyrppQeuAFYgAjG3oKbPavGvRDa2Sr+43d8ndntkFMXeJC3on2q1wuiXotBplrUpKXJ8SXwBWFEMHxHCiLQeg02hg8lbU2HpTALZt2zYAcjYpXuecc47fPuK1ceNGTJo0CZMmyfWeixcvxqRJk/Dzn/9c2eall16CJEm49tprQ+7j+eefx6hRo3DRRRdh3rx5OPfcc/H0008nNB6iXCZil0gZsFQKt26HyIBFGoa4Yml19nAGzCafbInsViRim06bL0vDk7LYvb39hLIGXKQ29ESZJLKwQ6vk7myiciDWq/uUfUSlgrj416+sAPoUVHn0dlUlvl4QyVyMjJbV0mo1Ib9/c1XMc8CamppgMBjQt2/fuJ+kb9++MBqNaGxsjPuxADBz5syQ7e7V7rjjjogLK1dWVuKFF15I6PmJ8onbI3/IpSto8C1+6f9vWJJ8iyuGfaw29GOzoQRRnQFjABY7dXAVePJjLtQHbUOUCeI9OKTKvwuyjgFYzgrMuA8oZ/lhKhQbfVmvE+3Ba37Gyh5DViufqiRiDv07OjpQVlaW8BOVlZXF3BmEiHqOuJKbrhMJoy7cHDD5/5GuKBv0oe9LdQDWGUcJolkpQVRlwHhSFrOqYt/V0sDSUmUOWB5c3aTcJgKwYYEBGC+25Cy91v+Ud2BFdi8xlCs0Gg1+veB0AMDCMxJf89cWUOkSirhIlw/fETFnwOx2O3S6xGs7tVotHA5Hwo8notRwe7NJ6WocIYKocE04IpYgBmRIiow6WBzuoKYcyRLBVLQadMCXAWvzy+TwpCxW6li1qcvud58IwCyqvy/PdykTwmXA0jV3llIv8HN6IBtwpMw1Zw/G9FP6oMYcemmqWFwyrhZf1XdG3EZcJHW4PLA53SgwJB6XZFpc64ARUe5zewOfdJcgJtaEwxeAnX9aNS6b0B/fW7YV3fbUBmCi9KHAEL0oQDThUAcPvCoeO5eqnLSx0z8AKw1RAso5GpRudpdbmXc6NKgEMRMjolQI7LrKDoipNagyuYzid84fgQHlhTjY1I2RtaX40fIvg+Z/lxj10GgASZIrV3pNAGa1WvGPf/wjoSeyWq0JPY6IUsuttKFPz4lt9Dlg4R87cVA5BpQX4pwRVXj4ytOx/kAzAKDTntr6b5GdC/yCDkVkwJo6fRn9dDU0yQcujy8QD8yA6bQalJr0fn/fWP4mRKkksl8aDVBdYoJRK8Hhkf+Ns+Np7gq8mMMMWHYpMOhw1ZmDlN9f3HAE6w+0+G2j9X5HdNhc6LA5UV2aeMYt0+IKwDo6OvCtb30roSeSJIndg4iygJgDlq4TifBzwKJ3FetrLsAnP7pQ+b3YWyLYZU9d/bfbIynZOLHGSCSiBEJ9ZS5KjyBSUQfiv1k4Puh+c6HBLwBjeSelm5jgX2rSQ6vVoMQAtHivFbAEMXcFfpYM4hywrDZteJ+gAAyQKyU6bK6cXy8yrkuLkiQl/B8RZQclA5amE9vwCzHL/48nEBTZpy5b6jJg6tLIWBaeDtWqXgS1FJ3L+z6YMqwSc8bWBt0f2AglXZlaIsHuki+uiPKmYtU/eTbcyV2GgM+S2jKuAZbNbjtvmDIvWM2sNGvK7U6IMWfADh48GH0jIsp6IgBL3xww70LMCTThCCSaZKRyDph6XIkGYG4GYDFzeo9VRZEx5P3mgONrZAaM0sxXpi2/90oMEgD5Z873zF3qi45GvTamz3vKnGKTHtecNQhPrT3gd7v4jsj1DFjMAdiQIUN6chxElCaBJxc9TZkD5gqcAyb/P56SnhLvB6/D7YHd5YZJn/wEXKdfABZ9LCa9Dka91i9zpp7XRJGJDFi4DGzgFU824aB0cyqdYuX3njoDxhLE3KX+zlOvXUXZyxSiyYY5T5Yr4TcbUS8jgoV0zQFTuiC6/bNWHo+YAxb7voqNvjOhVJUhikDKqNPGPE81MEvjcjMDFitxrMJdfQ4OwHjCS+kVeJGgRPWW5Nsxd6k/34uMbAKeC0J1Jq4okv9BtnTl9tJWDMCIepl0Z8B8c8D8g5RE5oDptBoUea9cdqWoE6LIgMWS/RIC26VzDljsnN4LAOHef4FzwALnbRClkscjYe2eRrRZfCdzroDPyGK97983uyDmh0JmwHLC6Fpz0G19SuTOh83dDMD8PPHEEzjjjDNQXFyMyspKzJo1C++8806qn4aIEqTMAUvXQsxiDljQOmDxzwEDfPPAOlOUAVNa0MfQAVEInAfGOWCxcwWUdwUKzIB52MSJetCyTUdx4zMbcM3T65XbXAFLdagzYCxBzA9FDMBywsyR1fjFFeOw/M5pym0iAGsMWMYk18R8xrFt2zYMHz4c48ePh90e+kXfeuutuPvuu7F161ZYrVa0tbVh9erVmD9/Ph5//PGUDZqIEhd4dbenGcK0ofetAxbfOMQ8sFRlwMQizPFMyA4MwMQx3X68HX9+fy+sjtQuFJ1PomUc+wV0JnMzAKMe9NoXdQCAr+o7ldtcAe9RdkHMPwzAcoNGo8ENU4dg8pBK5bY+3rW/mjp7SQC2evVqHDp0CGeddRZMpuCFz15//XUsXboUkiShoKAAs2fPxpVXXomSkhJIkoTvf//7OHToUCrHTkQJcKd5HbBwAZhIGsW7PmCp0gkxVRkweSBxZcBMASWI3tf2/WVb8YdVe3D3i1+kZGz5SGlwEKa0cMqwKr/fPcwuUg8KNccwsAlHEZtw5B3OActdfYrlDrpNvSUD9tFHH0Gj0eCKK64Ief8f//hHAEB1dTU2btyId999F8uXL8fOnTsxdOhQOBwOPPPMM6kYMxElId0ZMN9CzAFdEJFYCWKZt315quq/1U04YhWuBFFcRX9vVwMDhzACswuBBlUW+m/P40g9SP05KLLygUt1FOh870E25cwPnAOWu5QMWG9pwrF3714AwDnnnBN0X3t7uxKg/ehHP8Lo0aOV+wYMGICf/OQnkCQJq1evTsGQiSgZHmUOWHrOJMKvAyb/P95MXD+zXKJ2os2W/OCgLomL/XgENooQQcJpfUuU23J9gnBPUS4AhAnANBoNnv7mZIwbIE++ZiBLPUm9rpfFWzosOsWKzy6T6lydTTjyQ1GI9uaUG8QcsHarM2hueS6J+YyjoaEBJSUlqKqqCrpv/fr18Hg/sBYuXBh0v7htz549iY6TiFJEnACnay6D0btWl9MVbg5YfPvrXy5nSE60W5MfHHyBYTJNOERWR92OvqEjNQFivhEBb7gSRACYM7YWv1k4HgAzYNSz7KrPJVHSFFgmW8AALO9wDljuKlddAO2wOfH9ZVsx/88f4cPdJzM4qvjFfMbR0tICbZgvzE2bNgEAamtrMXjw4KD7KyoqUFRUhPb29gSHSUSpkv6FmOXnCZoD5v013jlg/crlDNjxthQFYK7UtaG3OX3NN+rbGYCF4lsHLPLxFpkJdkGkntRu9S3mKkqalHXAtMEZMHsOX3En3xpSF4+rzfBIKFFarUZZH8zqcGNvQyd21HUETXPIdjHPQiwpKUF7ezusVisKC/1r9Ddu3AgAmDhxYtjHGwwGuN3sDEaUaa6A+Q09Lfw6YPEvxAwA/ctEBix0gNNld8HqcKO6NLhZUCiJlCCGmwNmVQdgzICFpKwDFuV4i5NftvinntRm8QVgzd4MWGCZrFH1VlVfZKHc8/73ZuJwczcmDa7I9FAoCYUGHWxODywOt1LuX+ltzpErYj7jOOWUUwAA7777rt/tTqcTa9asgUajwbRp00I9FDabDR0dHaisrAx5PxGljyfKHJxUE4FNquaA9S8Xc8CsShmj2lVPrsNZD78XcwlgIuuAmcO0obc5fa+RJYihKeuARbkAIN4XLEGknqRegFkEY0oGzPvZpf6IsjhS032VMqOy2MjgKw+ILpZWpxut3gCsKl8DsIsuugiSJOGhhx5CV1eXcvtf/vIXtLa2AgDmzZsX8rGbNm2CJEk49dRTkxwuESVLTDBPdxv6wMmyic4B6+fNgHU73OiwBp8M7TrRAQD4z+bjMe0vsS6IwW3oJUmCzcUSxGh8DQ4iH2+lBJEBGPUQt0dCh2pBd7G2oAj6DSE+nKxOliASZZroYtlqcaDb2zynIl8DsDvvvBNFRUXYunUrTj31VFx77bU477zz8P3vfx8ajQZnnXUWzjjjjJCPffPNN5VtiCizsmYOWIILMRcadUodf11AIw51RmzT4ZaY9udIZB2wEBkwu8sDdULOwsWYQ/KtsRTbHDBmwCgRNqc7aqMe9fwvwLe2oK9MO/gzwcoMGFHGiSYqx1vlf+MGnSaoMiXbxXzGMXjwYDz11FPQaDRoaGjAK6+8gk8//RSSJKGkpARPPfVUyMe5XC48//zzAIALL7wwNaMmooQFrnHT00RgEzh5PdGFmIHwnRDV84X2nexCLJyuROaA+WfA3B4J9oAr44EBp5rHI0W8P5/89cP9uOSPa9HY6Z1fI+bcReiCCLAJByVn8StbMO1XHygZ8VDU5YeAKgMWYa06K+eAEWVcgXcZAdGMq6LImNC5RCbFtRDQ9ddfj/Xr1+O6667D6NGjMXr0aNx88834/PPPMWHChJCP+eijj1BTU4MpU6bgoosuSsmgiShx7jTPASv21mpb7P5Xjj0JliACvjLE4wFrgannmcWaOHGkoAmHyyMFnZhFah5x5ZPrMeWX78Puyv+Tud+88xW+qu/E//13O4Do64AJOjbhoAR5PBJWfFkPAFj6ycGw27UFZMBEABYpS2tlZpso4wIzYLnWgAOIowuicOaZZ+Jf//pXzNtfcMEFSpdEIso8cQKcrjlgxSb5Y6YrIACTEmzCAfg34lBzunwn6xJiO3EXGbBkShABoDugNMkZIXDYeaITAPDViU5MGFQe8/PmstVfNQJQrQMWbQ6YRmTA5NLSXLu6SZlzuMWi/Bzp86XdEjoA85VpB79HbZwDRpRxIgA71ir/W8/FACyuDBgR5b5IJxc9ocQbgHU73H5ztJLJgPlKEP0zYHbVUheeGM+TlC6IcWQETfrgRTy7bP4BmCtMiaG6oq43xRRWp/z3F10Qox1vdYkss2AUj23H2pSfDzV3h92uzepfgijmgClLJYT4cBpSVZSCERJRMkQJ4u56+WJmrbkgk8NJCAMwol4m3XPAir2rmLq9jSoE3zpg8Y+jX5n8YVsXmAFTrTUW64Kp9gRKEAFg/un90L/M96HfbQ8MwEIHDepbNcj/CGxghW/dyGOtVl8GLMY5YAAbcVB8th9vV37ed9I/AHO5PXjiw33YdLjVbw0wQD0HTJQg+t6jr905FQsmDcAjV4WebkFE6SMyYKID4ozTqjM5nITEXIL4j3/8I+adFhYWorq6GmeccQbMZnNCAyOinpHuOWBivQ5ADlLElatE1wEDfBmwwC6I6lb3jhjnV4myxXhKEAHg8evPgNPtwak/fRsA0BkYgIVJwXl6WQZM/ffdUdcRdxdEgI04KD7bjvkCsFaLw6+E9YOvTuK37+wGANxzkbw0zoDyQhxvs0ZswjG2vxl/uHpiOoZPRFGozyt0Wg1mjszjAOzmm2+O+0q1RqPBnDlz8Ktf/Spskw4i6llfHGnFmj2NWHTBKTDotGmfA6bTalBo0MHqdKPb7kZViXx7ouuAAb4ArL7dBo9HglYb3Oo+cOHncEQjjHgzYIB/iVJnYAlimKxNb0vmqMsHvzzeFvM6YOr3J0sQKVYej4Qddb7OhyLzLi78qJeH2HtSLl8aUCEHYC1dDnz3xS/w3611ANJXJUBE8RH/ngHgrKEVKC/K8zlgkiTF9Z/H48G7776LadOm4c033+yp10BEEVz5xKf443t78eSH+wH4Ag5TnBmfZIRqxJFMBqxvqQlajVxy2NRtV24/3OybfB+48HM4Yo6IaOwRD41GDi4B4OG3dvrd5wxTgqgeVW/IgKkzgS9uOKqUfUVbh07POWCUgIPN3eiyu/wWVld3LhSlSwCUTomiTLau3aYEX0BiF2WIqOep/x3PGt03gyNJXMwZsIMHw7dyDWSxWHD8+HF8/PHHePrpp1FfX48bbrgBe/fuRXV17qUJifLBe1+dxF0XnoJW7wlwWaEhyiNSp8SkQ1MXYHGoAzAxByz+/el1WtSUFqC+w4a6NhtqSgvwn83HsPiVrar9y6VEkbrtSZKEXd6OhKP7JVYu/Zuvj8dPX/1SOa46rQZujwR3LCWIvWAOmDoR2dLta3oQtQsiAzBKgJj/NW6AGTvqOmB3edDtcKHC2yUt8L1kLtBj9ui++M/m40H7Stdi9UQUH3UANntMngdgQ4YMiWvHo0ePxqxZs3DPPfdgxowZ2LlzJ5588kn87Gc/i3uQRJS8Q03dsDrdSmaoIo1tW0W9dqoyYICcsarvsOFEmxX17VZ8f9nWoG0cUQKwxk47Wrod0GqA0/qWJjSOyyb0x9Rhlfj56zvwzo56jOxbip0nOsI24ehtc8BEIHrPRafiT+/vVW4PtcitmkajgVYjHy8GYBQrMf/r9AFlONjUDbvL41d2KEqDh/Upxn/uPAfmQgO67C7lvabGEkSi7CTWFz21pgRDqoozPJrE9Hh+vaKiAo888ggkScK7777b009HRGG0W51K+ZdBp0GxMbiVek9RWtHbfSdCyhywBD+F+nnngW2va8d3X9oScm5VtDLEnSfkuSLDq0v8asrjVWMuwJPfnIy1P7gAD10+FoCvlXUg9Th7Q28JETxdOqEfTh9QptxeEUPNvrIYc284UJQSe092AQDG9DcrF37UAZh4Pw6sKERFsRE6rQZlhQY8ctUE/OmaiSg1+a5LswSRKDtdMKoGl07oj5/MH53poSQsLZ8uF154IfR6PXbv3p2OpyMiFfVV3FaLXAJWXmRM68K2ohV9tz24BDHhDJi3Bfyn+5vhcHlCNvOI1opelB+Oqk0s+xVocFWR0k3RHUMGrDd091OvO/fIVRPwtfH98NQ3J2NQZfT1lMR7gxkwipXF+xlTVmhQPnfueekL5WKMyIAFZrcWnDEQl08cgBLVIuvp6hRLRPGpLDbisWsn4YKRNZkeSsLSEoAZDAaUlZWho6Mj+sZElFLqTMPxVqv3tvTN/wKAImUxZlUA5o2NEg0ERSfE/d4r3pXFpqBtomXAdnkzYInO/wpFrG/lDNcFUf1zbwjAJN8J78jaUvzlujNw8djamB4r5uAwAKNYiYsuJoMOhd4M2OFmC176/AgAX0lsuPldJeoMWJoWqyei3idtny7d3d0oKuIK8kTppu52uK9RDlbKC9PbsrXEKEoQQ2XAEttnvzI5AOvwtn/vUxL8mqJnwOQAbEwKAzAxt8kVpg2+5JcBS9nTZq1k1p3TMgCjONmcvi6v6jLrA41yt9NwGTCh2OS/vhARUU9ISwC2detW2Gw2DB48OB1PR0Qq6jbg+7zZovI0Z8AKvSdCVqd6Dpj8/2SacKiFek2RMmA2pxsHmuSTslRmwMRJW7h1wNy9tARRl8DfmRkwipfNu8xGgUHn1+RGLL+hLokNpZQliESUBmkJwB588EFoNBrMmDEjHU9HRCrqk9f93qvAsTRASCUlAHP4AqJkM2CBbfRFVyS1SIsx7zvZBbdHQkWRAX3NweWLiRIT98N1QVTf6snzwMLjkZQsXyLZBDbhoHjZnd4SRL0WXaqmP2IJBPHvMtz7sYRNOIgoDXrs08XhcGDt2rWYP38+XnvtNWg0GnznO9/pqacjojDUCwIfEBmw4jRnwAwiA+YrQRSjSnQOWGB7+YIQXR0jZcBEB8RRteaUNiQRV81dMXRBzPP4yy9wSiQAYxMOipcoQSww6JSGHABwtEWe/+qOUoJYwhJEIkqDmNcBGz58eMw7tVqtaG5uhtvtu/r0v//7vxg7dmx8oyOipKlPXju9JyTpzoAVKRkw32dCsl0QDQEnR0Uh2shHCsDEnJCRKeqAKIiTNqdbgiRJQcGdOpbI98BC/foSOZllCSLFS8z7LDDo/OacHm2xQJKkqHPA1F0Qo61VR0SUqJgDsEOHDiX0BBUVFXjooYewaNGihB5PRMkJlYkpL0xvBkyssWXxC8Dk/yd6kTkwA1YYKgOmuggUSFwpV1/xTgV15zSPBASew/mvA5bfgYU6cAo35yYSNuGgeEiS5OuCqNf6LfzeaXeh3eqM2gVRvQ5YIu9ZIqJYxHzmcdNNN8W808LCQvTp0weTJ0/G7Nmz2f2QKINCzUUqz1QGzBliIeaESxD9HxcqABPzQUIR88OM+tSeZKnH5XR7oNPq8Ou3v8Kn+5vw/C1nBrShT+lTZx11CWIi57LMgFE81F1PCww6/OxrY/CDf29TbjvaYo2aARs/sFy5f1if4p4bLBH1ajEHYEuXLu3JcRBRD1CX3Kilex0wMQfM5gwuQUx0+lXgGj2FoUoQIzThEOWJqZ5or96fOPZPrtkPAPjv1hP+JYj5ngFzJ5cBE5nTbkf4TKbQ2u3AgaYunDG4Iq2LjFP2UF9wMem1uOrMQTjnlD6464XN+OJIG462WlRdEEO/R2aN6Yt1P74Qeq0W1aUmOJ3OtIydiHoX5teJ8li4xEFFcXozYKJBhl8JovdcKeE5YIEZsBABWKR1wJw9lAFTX1l3B2QfnW5PQBOOPA/A1BmwBP7MtWXyUgMn2qxRt/3Bv7di4V/XYfvxjvifiPKCaEGv02qUCyEDygsxuFKuwjnaYlFlwML/u+9XVojq0tR1RiUiCsQAjCiPOcNkgNK9DliRIbgEMdkmHIElREVxdkEU9xlTPNFefWXdGWL+ndQL54DptJqEslL9y+XFtuvabVG3FV3uGjqib0v5SWTACgIuqgyqkAOwIy2WpBYGJyJKFQZgRHks3NyZ8sJMrQMWaiHmxPap0Wj8smAFBh0mDCzz2yYTGTCNRqMEYaHm37mhypCFH15eSGYRZgDoH0cGzOJd4iDfyzopPJEBMwVkwwdVyoH80VZr1HXAiIjSgQEYUR4LNf+r2KhLedARTWGEDFgy83XU84oKjTos/dbZ+O3C8bh8Yn8AQKfNiTaLAz97bTu+ONLq91h7D80BA3wndw6XB+1W3xwSCf4ZsLwvQYzS8CCafmUiAxY9ABPBfb5nFSm8aBmwYy2WqF0QiYjSIbX9l4koq7hCpFjS3QERCJ0B85UgJr5fvU4DeOObIqMOlcVGfOOsQTjQJK/x1W514hdv7cK/Nx3DP9cfxqFfz1ce21MZMEAO6uwuD775zGeoDyif601t6F1RGh5E069cZMCilxWK+YVsmNh7hc+AeQOwVisczIARURZgAEaUx0KVIFYUp3f+F+DLgNldHrg9EnRaja8EMYkTIXX2qkB10lXmXees3erE/pNdIR/bU10QAd/8ksPNFr/bJSlwIeaUP3VWEe+/RP/GA8p9GbBIwaokSUp2lS3rey+RATMFXFTpV1YAnVYDh9uDE95sKjNgRJRJLEEkymOhW9BnLgMG+FrRpyQDpnpwYYgArMPqCtvn3um9Em7siQAsTIc1CQhYByw/g4UDjV041NQdteV3NKILos3pQZslfDtwu8ujBPT5ekwpOvHZUhCQAdPrtOjvzaYe8mbHI3VBJCLqafwEIspjobIBoboF9rQCve85A0vFkpkDps5emfShAjBn2ABP6YLYAyWIkQKOfG9D73B5cPlfPsFlf/kYXXa5MUaiGTCTXoc+JfIFg+MRGnFYQjR3od5HKUEM8W9azAM75M1KMwNGRJnEAIwoj4l5TurskFGf/gBMq9Wg1CRXPIsSoGTb0AP+a4GZDL6PM3UJYri9i2PTkyWIgSRJyvsAzOpwo9PuQofNhQ0HWwAkd7IrGnGciNCK3uJwKT/n4zGl2CgliCHWBBRrgQmcA0ZEmcQAjCiPiQxYgUGdKcrMP/tzTqkCAKzc0QAg+Tb0gFxaJKhfl18AFibAs/dgBixcUCchIAOWh3PAXKoX9fG+RgDJBdn9RCv6CJ0Q1c1dOAes9xKBeIkpOAAbFBCAcR0wIsqkpM88LBYL/vznP2P+/PkYN24cRowY4Xd/e3s7XnjhBbz44ovJPhURxUnpQhcmUEmnueP6AQDe3n4CQGoyYOrMSqgSxPYIJYi+DFjqT8QiXV1Xhwf5uGaVOgD6ZF8zgOROdsVizJFKENXLG+ThIaUYdXpLXouNwf3FBlYU+v3ODBgRZVJSXRC3bNmCyy+/HMeOHVM6VAVebTabzfjFL36B3bt3o2/fvrjwwguTeUoiiuLFDUfw2Pt78cy3zlIWHVUHKuleA0y4cHQNDDoN9jd2Y9/JzpSUiql3EaoE0ep0K802AjncoTumpUK4kjtJAtTDycc29M4QGahkTnb7x9CK3hJieQPqfbps3gxYQfQAjHPAiCiTEj7zaG5uxvz583H06FGcccYZeOSRR2A2m4O202g0uPXWWyFJEv773/8mNVgiiu7H//kSde02PPLuHqUcTH0C3KfElJFxmQsMOPeUPgCAt7+sV0rxksmAqTNI6kYfpQV6pflhpy109zxnD7ahj7RP/4WYU/7UGecOEfDqkipBFHPAYitBzMdjSrHp9mbAxHxTtcDur+yCSESZlPAn0KOPPooTJ07goosuwmeffYbFixejsLAw5Lbz58uLn65bty7RpyOiGKgzKsUmnVIOZtBp8f+uGIfzTu2Db00fmqHRAZeMqwUAvL29XhlrMudBHtXZtrqUUN30o8PmCnoc4MuA9URGcGifYmg0UDr4qamnfeXjfCVXiIltqciA1cWYAcvHsk6KjVKCGCIAE1lxgRkwIsqkhM883njjDWg0Gvz2t7+FNsoZ1MiRI2EwGLB///5En46IYtDc7VB+7l9eqJTf6bQafHPqEPzz1ikoCjE/Il1mj5EDsJ0nOtDqXdcpVRmwoPJn7wlXqAyYJEnKsemJDNgjV43Hmu9fgImDyoPu8+R5CWKooDK5AEy+sFffYQsbsKq7IObjMaXYRCpBDAzAOAeMiDIp4TOPAwcOwGg0YuLEiVG31Wg0MJvN6OjoSPTpiCgG+052KT9rgKQXwk21ymKjMudKjC2ZobnCzO8CfCdcNmdwRkZkv4CeyYCZ9DoMrioKCi4D29DnYwZMBLbql57MyW5NaQF0Wg3cHgmNXfaQ26ibcHjy8JhSbMS6cyUhMmB6ndavNDFbPhOJqHdK+MzD4/FAr9fHtIiqJEno6upCcXFxok9HRDHYfrxd+dnm9CjlYNnUcjmwPCiZhZgjZTsCr3irqRtzGHsgAyYEHnePFLgQc489dcaIoFJ9/O0hguBY6bQapZSzqdMRchu/NvR5eEwpNpECMMCXFQeYASOizEr4zGPAgAGwWCw4efJk1G0///xz2O12DBs2LNGnI6IYfHG0TfnZ7nIrJ8PZNOG8yOi/Rk+qShADRQrAHC5fQNATJYhC4Gtze/J/IWYR9KtbgVucoefhxUosJG5zuUPer54DxhLE3itaAFZe5PtMyKaLUkTU+yR85jFz5kwAwNKlS6Nu++CDD0Kj0WD27NmJPh0RReHxSPjicKvyu83pUTI92VRuE7hGTzJDc0dIrMQSgOm0mh69Eh543N0eya8JRz4GYL7GL77Xrs5QJaJABGBhMml+JYh5eEwpNpHmgAH+AVg2XZQiot4n4U+ge+65BxqNBr/85S/x3nvvhdymoaEB119/Pd5++20YjUYsWrQo4YESUWT/3VqHunZfpzj/DFj2BGBFptRlwCKdbIcKwMT8ILEIc0+WHwLBJ3luj5T3bejVjV8ES5IBmMkbgNmd4TJgvgxbPh5Tik2X0oY+9MUX9WdCNl2UIqLeJ+Gzj7Fjx+KXv/wlOjs7cfHFF+PMM89Ee7s8/+S6667D9OnTMWTIELz00ksAgD/96U8YPHhwakZNRH5sTjd+9+5uAL4FR9VzwAxZVG4TmAFLIv6K2MTCHCIAc3qPh92VnuMSGN+5JclvjlI+ZmvUSx8ISQdg3kYpdlfoDJhfG3pGYL2S2yMp74PigIs8Qlmhb1mIbLooRUS9T1KXf3/4wx/ib3/7G8xmMzZv3gybzQZJkvDyyy9j3bp1cDgcKCsrw7PPPos77rgjVWMmogD/Wn8Yx9usqDUX4M6ZIwDIGTCXO/vmgBWmcA5YpI53oTJg4ngoGTB96BO1VAmdAfO93nzs2Bdq8e9kFUSZA2Zzcg5Yb6d+DwR+xgh9zb5F6JkBI6JMSnpBoFtvvRVXX301li9fjk8++QR1dXVwu92ora3F9OnTcdVVV6GsrCwVYyWiMJZ+cggAcN/sU1HiLb+xOz1Z14YeAIpTGIC5EgzAur2lSoENQVItMAPmCpoD1qNPnxE98Z4r8GbAbE4PCkLcr86A5eMxpejUjXXClRYPrChSfmYGjIgyKSWXxUtKSnDTTTfh6aefxptvvom3334bS5cuxW233ZaS4Gvt2rW49NJL0b9/f2g0Grz22mtB2+zatQuXXXYZysrKUFxcjLPOOgtHjhxR7rfZbFi0aBGqqqpQUlKChQsXoqGhIemxEWWDFu8CzOeM6KMq13IrAUo2BWBFptQ14Yh3DpgoQRQLVlcWG4O2SSV9QAbM0yu6IKZ+3qHIgMVSgpiPx5TCs7vckCRJWdtPq5HX/AplgHdRbyD43yYRUTrlxCdQd3c3JkyYgMcffzzk/fv378e5556LUaNG4cMPP8S2bdvws5/9DAUFvmul9913H9544w0sW7YMa9asQV1dHRYsWJCul0DUYyRJgt1bmmXSa/06xmXlOmABWadk1gGLOwDznqSJgLWqhwOwwOyeKzAAy8N0jcgyhjsJTkSBQWTAQpcgqrss5uMxpdCOtVpwxkOr8JNXtysZsEgLq4v5sUByc0+JiJKVcAliZ2cnVq9ejdLSUlxwwQURt/3ggw/Q1dWFCy+8ECUlJXE/19y5czF37tyw9//0pz/FvHnz8Nvf/la5bcSIEcrP7e3tWLJkCV544QVceOGFAOT2+aNHj8b69esxderUuMdElC3UJ/UmvQ4mgy8D1m2XT0xFUJYNilLahj6xEsTmLjuANGTAdNHa0Pfo02eEEvRrNSgwaMO2jo9H9AwYuyD2Ri9uOIJuhxsvbjiCW8+V1xmN1Nm0tsx3UbbD6uzx8RERhZNwAPb8889j0aJF+N73vhc1AFu2bBmefvppPP3007j11lsTfcqQPB4P3nrrLfzwhz/ExRdfjC+++ALDhg3Dj3/8Y1xxxRUAgE2bNsHpdGLWrFnK40aNGoXBgwdj3bp1EQMwp9MJpzM7P6jFuLJ1fNkuX46faL0MAFq4oYd8Bmp1utHcJbelLyvQp/R1JnPsCvQBQYnbBaczsWzJt84ZgiWfHMbcsX2DxlIUohO11e6A02lAY6d8XMoLU3tcAmkCMnROt9svQHC63Dn//gvk8C66rNUAI6qLsaOuE0By/8681xRgsYd+36lLEF3u/DumqZAvn3dqBaoLHG3d8r9po14b8TVOHFSGw80WjK4tjulY5ONxSycev+Tw+CUmk8ct1udMOAB79dVXAQDXXntt1G1vueUWPPXUU1i+fHnKA7CTJ0+iq6sLv/71r/GLX/wCv/nNb/DOO+9gwYIFWL16Nc4//3zU19fDaDSivLzc77F9+/ZFfX19xP2vXLkSRUVFEbfJtFWrVmV6CDkt149flxMQ/5TfX/ku6q3y710WG77cfQCAFiePHsCKFftT/tyJHLsD9RoAvozc+++9h6IEP4nGeoC7xmgwpPg4Vqw47nefHOj47/j9D9egfxGwfa8W8nHZjxUr9iX25DE4eER+HuHI0WNQJ8X27d+PFSv29tjzZ8Lmk/Lft6WpEQuHeeCxaDGrvwcrVqxIeJ/HvMdx34HDmDg8+H3X0a0DIB/Yvfv2Y4Uzv45pKuX6553avjrfZ8nr738KQAe3wx7xvXbTAMDVD1j7/sq4niufjlsm8Pglh8cvMZk4bhaLJabtEg7A9uzZA4PBgIkTJ0bddvLkyTAYDNi9e3eiTxeWx1vucvnll+O+++4DAEycOBGffvopnnzySZx//vlJ7X/OnDkwm81Jj7MnOJ1OrFq1CrNnz4bBEHrhSQovX47fiXYbsHEtjHot5s+fhyMtFvx668fwaHQoraoCGhsxZdI4zDtrUMqeM5ljp9lej2UHtym/XzxnNkoLeub4/+yLD/wyhNPOORdj+5vxyrObgKZmTJ88AfMm9e+R5waAfR/sw8rjB5Tfa/v1x8kTdcrvQ4cNw7xLRvbY82dCx+fHgP070b+2L25cMAk3pmCfR9YcwLvH96G63wAAR4Pedz/8/D3AW9yZj8c0FfLl807tq/f2AocPAgBKBpwC7DuIstJizJt3bsqeIx+PWzrx+CWHxy8xmTxuHR0dMW2XcABWX18Ps9kc0wR6rVYLs9kcNduUiD59+kCv12PMmDF+t48ePRoff/wxAKC2thYOhwNtbW1+WbCGhgbU1tZG3L/BYMj6N30ujDGb5frxc0NuKGHSa2EwGFBSKK91Y3d50GaVg48+pYU98hoTOXZzxvXHwJV7cazVCgAwGo0wGJJeESOkskKDXwAGrQ4GgwGtFrlEoLqsZ46LYND7vy4JGr+FmKHR5vR7LyTvd4JBr/v/7d13eBzV2Tbwe7ZKWnXJstwLxtiAsQU2BptiY2xsB0NooQVIcIBAaIEQAgmQDiEkLyUOBAiBEOBN3g9CMdXYGFOMce8F9yqrd2nr+f7YndmZ2V1pNdtX9++6uJC2Hh9tmWee5zwnbv+23MDWCm55k2fV687rE5q1YVI2zmkcZfrnnVq7K/h331Pn/zyxx/F1p5ZN85YKnL/YcP6MScW8Rft8httU5eXlobm5GV5v+K5Uah6PBy0tLbDZ4r/g3WazYdKkSSHZtR07dmDYsGEAghm4xYsXK9dv374d+/fvx+mnnx73MRElk9wZzh7YVDgn8H+fAGoDzSaKwy2ISpEcqxnnnxTMOiWyQ36hrhGH3AVRbtqQb09M4CfTt2L3+ATU8Vc2tkxPZBv6cA09OnWdEdmEo+9oVjXS2FXbBqD7LohEROnC8NHHsccei5UrV2Lx4sWYNWtWt7ddvHgx3G63pjNhb7S1tWHnzuA6jT179mDdunUoLS3F0KFDcc899+Dyyy/HWWedhenTp+ODDz7AO++8g6VLlwIAioqKMH/+fNx1110oLS1FYWEhbrvtNpx++unsgEgZTz77L+//JXdBBIDqZv/C9JK8xHb7660xlQXKz7FsxNyTolztR5wcgLm9ydkfTf/4Pp+Aql8EsjD+SsxGzEoXxNATfuoOiEB2BrWk5fUJ/O7drXhrXbCcd299OwAGYESUGQx/Us2dOxdCCNxzzz1obW2NeLu2tjbcc889kCQJc+fONfRcq1atQlVVFaqqqgAAd911F6qqqvDggw8CAC666CI888wzePTRRzFu3Dg8//zzeP3113HGGcE68P/5n//B+eefj0suuQRnnXUWKisr8cYbbxgaD1E6ccoZsEDgZVcdgMjBWboFYGMHBNdVJnI/nkLd2jK5Db0cJFjjuFdVOOEyYJ2qGKK7NvqZStn8OyH7gIVmwLpc2ssYgGW/d9Yfxgtf7NFcJp9U6a4NPRFRujD8SXXrrbeipKQEmzZtwqRJk/Df//4XnZ2dyvWdnZ144403MHHiRGzatAlFRUW44447DD3XtGnTIIQI+e/FF19UbnP99dfjm2++QWdnJ9atW4cLL7xQ8xg5OTlYsGABGhoa0N7ejjfeeKPH9V9EmSCYAfNnCSRJCjkLnE4liAAwun8+rjltGL4/dbgy7kRw6EoM5T2qkrVBtT4A8/oEurzBy7IxWPB4g/uAxYtcVtsVLgPmZgasr6kJbCMRDjNgRJQJDJcglpaW4rXXXsO3v/1t7NixA5deeinMZjPKy8sBAHV1dfB6vRBCICcnB//5z39QVlYWt4ETkZ++BBEAciwmuAKX51rNabURM+APEn/z7RMT/jx5Nu2/2+XxH5wHSxCTmwHzCoEuVQyRjcFCIteAucJkwNR7gAFcA9YXdFe2zACMiDJBTJ9Us2bNwhdffIGpU6dCCAGPx4Pq6mpUV1fD4/FACIGzzjoLy5cv12yCTETxI6+LyVGt/VIHXCVplv1KJn0ApmTAAlkaayoyYKqEjS80nsh4iVgDlmvzv7Y73KEZsE59AMYILOt1F9wzACOiTBBzC7Cqqip89tln2LlzJ7788ktUV1dDkiRUVlZiypQphhtvEFF0nG5tCSKgbcRRnGbrv5Ipz6YrQQxkvtwJWKcUjlkKV4IY/D2bM2DxnFu5lLTdGa4Jhz4Dln1zSt0bO6AQW4/4996xcw0YEWWAuPVgHjVqFEaNGhWvhyOiKIUvQVRlwBx9NwPmsOtKEL3aDFiiuyDqz9S7vT44fcHLvFkYLCRibh2BQLpd1/EQCNcFMW5PS2lKv/XAcf3zlQCMGTAiygT8pCLKcHIJot0aPgOWbh0QkylcBsznE8pBesLb0OtKHNX7FgHZ2YY+EWvA5AxYl9un3cgawX3wZMyAZb8OXSZ0SGme8jMDMCLKBPykIspwXe4eMmB9OgALXQPmUaVIEl2CqG8W0NyZ/R37ErEGTJ3J1FchhpQgMgWW9fR/c00AxhJEIsoAUZUgnnPOOQCAYcOG4R//+Ifmst6QJAmLFy/u9f2IKDIlA6YKwLQZsL5bgqjPgLm9QmnEASS+CYe+y2KTLgOWzfuAmePYYdJuMcNqluD2ip4DsOybUtLRl50OKWEGjIgyS1QB2NKlSwEAY8aMCbmsN6RE7rhK1Efp9wEDtBmwvtyEQ78GzO31KS3ogWS0odf+Lm8NIMvCBFhwDVicg1uH3YKmDjc+OGjC1arLQ7ogZuOkkka77m8+tIwBGBFllqgCsIceeggAlD2+1JcRUWopXRCtETJgfbgJR0gJotenBAhAMtrQd38wmI3BgicBJYiAvxFHU4cbX9WYsONoK04YXAogmAFz2Mxod3mzck5Jq1OXAasszFEypAzAiCgT9CoA6+kyIko+l9d/AKpe+8AMmF+uNVwJYrBJRKKz8uGWo5glgXknDcSb649kZQmiNwFNOAB/9lLW6Vb/HAjA7BZ/AJaFe6uRlno7gismDYHZJGFQcS721ndwDRgRZQR+UhFlOHlvK3U2h10Q/dSbUwNyCWJyWtAD4TNgl43wYdLwEgDZuV4pURmwlq7g+jl1ow05G5Kf4w+2mQHLfvKG3N+eMBAPzTsBQLARh3oTeiKidGU4ADOZTLBYLNi5c2c8x0NEvSSvaVJ39LNruiD23RJEu1XfBVEoAWsyAjD9c9w3ezRO7y+UzFs2BgveMK/HeOhSZb06VK3n5RLEfLscgMX1aSkNdTj9Qfd3Jg1BbqDMeP4ZI3DOmArMGFuRyqEREUXF8Ddkbm4u8vPzufkyUYrJXf3UB/vqDFhfLkEs1f3b3V5fcL6SUKqkbkN/cdUgXD91OIBgaeKSbTUhjTkyXbjXY7x1qZowyCWI8nq/bAxqSas9EICpu5xOO64CL3xvEgYU5aZqWEREUTN8BDJ48GC43e6eb0hECRUsQQxdA2Y2SSjMiWqpZ1bKtZmx8LYzMGOM/6y4PwALLdlMFHUnQJMqIFEHZv9cvjfh40im2jYXAKAwN3GZ105VBkxuQiMfjDMAy24+n0Bd4DVWnt93Ty4RUWYzHIB961vfQldXFz799NN4joeIeskdpu23nAErzrX2+e0fThxUhAlDigH4g9VgCWJyM2Bm1c/qv8myb+oSPo5kEUJgd20bAGBkP0dcH/vRS09SflY34ZD3wcu1yhmwuD4tpZmGDhdcXh8kCagoyEn1cIiIDDF8BHLfffehX79+uPnmm3HkyJF4jomIekHJ6JhCM2AlDp4hBgBroDW12yvCBqyJoi7DU2fAvKpWfYOKs+cgsq7NhdYuDyQJGF4W3wDsOxOH4Nwx/QAAXeoMWKCEU26+IFQZsKMtXfjbp7vQ1OGK61godaqbuwAAZQ47W84TUcYyXJu0detW/O53v8OPf/xjHH/88bjmmmswdepUVFRUwGyO3IXorLPOMvqURBRGdxmwvtyAQ00OhDw+dQli4g/e1K3Y1U+3t75D+dlhy54SUTn7Nag4NyHd6AoCZY0dqjVg8hq6XJt/gtWt/a/5+wrsONqGlXsb8fx1E+M+Hkq+oy3+AKyyyJ7ikRARGWf4m3/atGmaMpoFCxZgwYIF3d5HkiR4PJ5ub0OUCjtr2nDDP1fhR9NH4dJTBqd6OL3iCdN17tiKAgDACQOLUjKmdCMHW8luQ6/OQDZ2BNfMDi/LU35uVwUTma46cHA8pCSvh1sakxcI6sJlwIIliMEAbMdRf0D4yfaahIyHku9IIANWWchmG0SUuWI6BSyE6NV/Pu6QSWnqoy3V2FPXjoUbDqd6KL0md52zqgKKU0eUYvl95+CB849P1bDSSjAAE2ED1kQZVBw8SNxV06b8PO+kAcoaKbmjWzaQs1F2a2LmVt7XrbPbACz0fkmItSlJmAEjomxg+FvS5/MZ+o8oHe2taweQmQfD4fYBA4ABRbmaEri+TC7P9Kjb0Cdpbp6/diKKcq2457zjlMusZhNuPHMkAKDDlXmvuUi8CdqEWZarZMCC3yWuQBMOec83X5gIrK83oskm1UoGLHvWThJR35M9iw+IYiCvyWntyryD4eC+VjzIjERuOa/NgCVnvs49vj/WPTgTkiRptu7IC2wc3JaBQX8k8vq6RAX+8rqy7jNgoQEYz0NkD7nMtT8DMCLKYIYCsJqaGqxatQotLS0oLS3FpEmTUFJSEu+xESWNkgHLwGyEsg9YEtqqZyr1GrBwXSMTLVwGJt/uDxjandmzBkzJgCWovDM3sNlyZ2DdnBACLq9Pc134EkRGYNlCzoBxw2UiymS9CsCam5tx44034vXXX9e0+jWZTPje976HJ598Erm5/FCkzNLh8qCm1QkAaMvADFgy26pnKnnPL48vuW3ouyNvHJyJQX8kniSVIMoZsE2HWiB/FeWGaUMvMzMAyxrVXANGRFkg6gDM4/Fg5syZWL16dcgXnNfrxQsvvIADBw7ggw8+iPsgiRJpX32n8nOb0wMhREatGQm2Vc+cMSdbsATRl9QmHN3JD5QgZuK6w0g8geA2USWIuYEmHF1uL7rcXsz7y+fKdXKDDm+YACyD3s4UQXOHGy9+uVcpE2cJIhFlsqiPQF566SWsWrUKQgiceeaZ+Nvf/oZ3330XCxYswPjx4yGEwKJFi/DOO+8kcrxEcbevIbgnk9srlDUlmUIJKFiCGJGmC2KYrpGp4AgEYB1ZVIKYrAxYq9ODlk635rpgE47Q+5kS+LdeuOEwfrNwS9jmHxQ/v1q4Gf/z8Q4AgMNmRkEO9zgkoswVdQbsP//5DyRJwlVXXYWXX35Zc92NN96IOXPmYPHixfh//+//Yd68eXEfKFGi7FNtigv4s2CJ2EQ2UdKlpC6dqbsgupPchCMSR2DNUrsr87KukXiVJhyJORkwqiIfgH9/L7lsWCav6QvfhCNxc/vHD7djX30H5o4bgFOGcS10onz2TZ3yc2URs19ElNmi/pbcsGEDAOC3v/1tyHVmsxm//vWvIYRQbkeUKdQZMCDzSsKCJYjMgEUiz43HJ5QyuVRnDOUMmE9o26pnskSXww4tzUWpXcDtFfh461HNdXKSK0z8ldAuiPLeZ4ebOnu4JcWiODeY8WIARkSZLuojkIaGBjgcDgwbNizs9ePGjVNuR5RJ9uoyYJnWil7JgLHXdkRyAObyBLsgpjoDlqvKsmZqK3qXx4f73tiI9zceAQB4fYldAyZJEkYX+f9+H2yqDrkOALYfbUVLlzvsdYkgZ9zkDYIpyOnx4q9Ld2LrkZaYH6s4LxiAcf0XEWW6qAMwt9uN/Pz8iNc7HA7ldkSZZH+YEsRMorShZwYsIjk49fhUAViKM2Amk6SUIWbqZsyvrzmI177ej5tfWQNAvR4xcQHPcYEAbFt1q+Zy9VPe+upapRxSf128yU8jt0enoL9/vgePfrAdc574LObHKsq1KT/3y2cHRCLKbDxioz7N6QWOBtaSjCj3n0TItFb03Ii5Z0oJojdYgpgOXSMzfTPm+jbtOixPgteAAVAyYHrqrNuyHbXoUm3WnMg29HJX4CPMgIXYcKA5bo8l75sH8LOOiDJfr/YB83q9OHDgQNh9VqK9zdChQ3s3QqIEqgscMxXnWTG4JBd76trR3Jk5WVwhRLCpBLsgRiQfsLnSqAkH4G9FX9vqRIcrMzsh2izB15wQIrgRcwJTTvlWYGxlAbbqMmD6MkN1AJbYEkT//48yAxYinqWo8vsWAHIsmdMkiYgonF4FYHV1dRg+fHjE6yVJ6vY2kiTB48nMM72UnWq7/AcIw8scKHX4S1wa2l2pHFKvqMus0iGjk65s6gyYLz2acACAI3BWP1MzYDZV2WuHy5u0bOyUY0pDAjD9sX6XajsJlzdxTU7kNWDVzICFiGcApt4e5KrJPJFLRJmtV0cgQoiY/yNKJ7WBY6YR5cEArD6DAjCPKgBL9cbC6cwSZiPmdAhY82zZsxdYU6c7KRkwAJh6TFnIZfqDfXUGzJXAvf3k/b+OtnT16b3AOl1eLN56VFv6GdcAzP+4f/7OeJRxDRgRZbioM2D/+Mc/EjkOopSoC2TAhpXlKQeNDe3O7u6SVtyqM/vsghiZ3HHQ4xPoDBwgpkPAmh9YA7b5cDPGDynC4JK8FI+od9RZpuYOt1Imlsg1YAAwcVgJbGaTJrOl3+srWQGYfF7R7RVo6HChvI8FB9XNXcizm/HQW5vx37WHcHHVIPz58gkAtH8Tr0/EFJDJGTA7yw+JKAtEHYBdd911iRwHUUrUdvoPCEaUO9AeyEJkUgmix6suQUx9QJGu5EAHCP590yFgzQt0Qfzr0l3469Jd2PPw3IzakLlTtXatOYkZsFybGROHl+DLXfXKZfppU++tlowSRMAfjPSlAKy5041pj32C4WUOpSvlG2sPKQGY+iOpzelBkWovr95yKQEYP+eIKPPxk4z6NLkJx7CyzCxBdAfW3EhS4vZeygYWs0kJwoIBWOo//tSBIaBd05cJ1Fmm5k6Xqgti4l+LD807AaeP9Jci/vDsYzTZFknSrqvz+kTC5lb9sH2tFX1NSxe63D5sP9oa9np13BvrBvdyBszGAIyIskCvmnAQZZNOlxfNbrkJR55SzpeJGTBrGgQT6a4wx4I2pycYgKXRGjCZV4iM+lDudOszYMlr8X9cZQFeu/E0tDk9yLdbsEMVBNjMJjR1aN/Hbq8PZlP8y9c0GbA+1ohDzixGWt6tDtBjbTQjrwFjBoyIsgE/yajPOtjUCQAoyLGgOM8W7ILYlnkBWDoEE+muMFD+JGc406EJh3pvIwDwJa5SLiHUJYiNHW7l9ZjoNWBqchZRHQTYLSa06LaTcCZoHZj6eY/2sQBM3Ro+nM44BmBKCaKVa8CIKPMxAKM+60CjPwAbUpILwB+IAUCbK3NagruVluqpDybSXWGOPwBrTKMSxDxdCaIvwzrFqg+wt1e3Jm0NWDgdqvetzWIK2c8vUY049GvA+hJ3N2vrnB4vVu5pUH6PdoN7t9enyZwFHy9Qgsi1rkSUBfhJRn3WgYYOAMDgQAAmH5ALgYxpJx1sqc63ck8Kc/3BjrxOKR0yYA79GrAMC8DUB8pr9jfCncQ1YHrqt6wQQFOHLgBLUCOOvlyC6O4mqP3FfzehVZX1inYN2LcXfIHpjy1VSg5lzsBrzW7lZx0RZT5+klGfdVCXAVOX8XkyJACTz0CzBLFncgZMlh5t6PUliJnxupOpM2D76jtQEwhAUvF6rBpSjLEDCgH4s136DJgzTFYlHvpyEw53N6/X/1t9UPN7axQBWLvTg82HW3CkuQu1rdrtQOQAmmvAiCgb8JOM+qwjgYOlgcVyBkwdgGXGYhyPUvLFt3JPCnUtsNOhbFPfhCPD4i/NGjAASivyVLweTSYJz183EQDg9IYGYE263+NB6DKWzIBFpl+TF05dWzDoUmdXhRDsgkhEWYWfZNRnyQdkpXn+A3OzKfMyYB5v8rrOZbrCHG2wkw5Zw0xvQ98Z2GtLLuOVpWpLBDk74vL4QkoQ6xPQXEf/52rt8sTcbj2TdLcGTK+xo+f5V2e91Pu4ub1CaXbCjZiJKBswAKM+Sz5AK87zdz9Un7X39tDdK13IByk57AzWo9AMWOo//uSNmGWZ1oRDzlJMPaZcc3mqsovq7IicTZEvq29zhr1PLML9vfpSJ8RI6+rCldI2dvQuA6Yub1U/D0sQiSgb8JOM+pwth1tw13/WYfvRNgBAiSoDJu/lmikZMPkAmAclPSvQZcDSIWuY8RmwQAnilFFlmsvNKZpb9fugJpBNGVnuAKA9uI8XdQCWGzgJ0pmgtWbpKFIb+nDlns1RBGDqDJi6vFW9fo9dEIkoG8Rlz8+3334bH374Ifbt24fOzk4sXrxYua69vR3r16+HJEk4/fTT4/F0RDG55OkvNQdJxXnBzIjFJMHtFRmzBszJvXGiFtKEIx0yYFnShv6EgYUozrMqWeWUZcBUB+fyvlPHVORjW3Ur6hJQgqj+c+XazOh0exO231g6ilSCqA52BxXn4lBTZ3QliKq/UbgMmM1sgikN1m4SEcUqpgDswIEDuPjii7FmzRoA/oWykqT9cLTZbLjyyitx8OBBfPnll5g8eXIsT0kUM/0Z6hJVAGaWA7CMKUH0/1tYgtizkBLEdMiA6ZtwZNixu/xeyrNZMGFIMZZurwWQuuBWkiTYzCZNydox/fIBBDfgjqdwGTCnO8P+iDGIGIAFMlnH9HPgwXkn4LoXvu51CeJNL6/G8vvOwYCiXCUzlp8Tl3PGREQpZ/hbsr29HbNmzcLq1asxaNAg/OhHP4LD4Qi5ndVqxfz58yGEwH//+9+YBksUbxZJKAdOAGANHDhmSilYV2CvnByWIPZInwFLh73T8nRt6DNpHzCvTyibG+dazRhQlKNcl8rgVl2GKEnAiPI8AIlaAxb8OSewP1WXp++UIEba3Lo2MNfl+XblBFdTL5twAMCv39kCAFh/sBkAcOKgIsNjJSJKJ4aPQBYsWIDt27fj5JNPxtatW/Hkk08iPz8/7G0vvPBCAMAXX3xh9OmIEsJhgSZrK69dSUUJohAC/165HxsDBxvRYBOO6MkbMcvSoQ29PgjMlMAf0GaSc21mTYYxVV0QAW0jjgK7BaUOO4DomkD0liYDZuuLGbDwr1c5kOpXYEdJoMlRNCWI+nV68uOs298EAJgwmAEYEWUHw/n8119/HZIk4c9//nPYzJfaiSeeCLPZjB07dhh9OqKEyNMmRZSD8lQ04Vi6vRb3vr4RALD3kW9FdR+nh004opWOGzHr6feVSmfqJgl2i0kzv6kMbtXvheI8m6o0MP6ZKaGKtZTn6UMZsEgliIeb/J0gy/PtyhrbLrcPXW5vtyeL9AGYHEzvrPU3TDp+IAMwIsoOhgOw7du3w2w2Y+rUqT3e1mw2o7i4GE1NTUafjighCq3aA1557Uoq1oBtP9ra6/swAxY9fRfEdMiA6WVSCaK8/jDXaoYkSWmZASvKtQZLA+McgL217hBeX3NI+T03sJ6PTTiAQ00dAPwZsHy7BVazf21tfbsLg4pzw95HCBFSgij/LTsCDVX0WWwiokxl+NPM6XQiNzcXZnN0B34dHR3Iycnp+YZESVRm1/4uHzimohTMLPX+oNWpNOFIv2xOurGYTXDYzGgPZG7SoQmHXiaWIMqld+qNrlO5vk59MqIo16pkprriHBjd8b/rNL/nBt6DfSkAi7QP2KGmTgBAeb4NkiShzGFHdUsXGtoiB2DtLq9m82UgmM3sVAX7RETZwPC3ZP/+/dHW1hZVVmvz5s3o7OzEkCFDjD4dUVx4dAcMpTm6DFgK14AZiL/YBbGX1FmadGjCoZdJXRDlEkT5oDhdMmDqvdWK8qzKe0NdMpkIOQksdUxXkSoFDjXKAZj/DFepw78OrL49ciMUffYLAOyWQPCsC/aJiDKd4SOQM844AwDw73//u8fbPvroo5AkCdOnTzf6dERx0e7UHhwV6taAyQeOqShBNBmIwFiC2Dvpsk5JTT44BTJrH7BOXfY1XebWoQ7AcoMBWJfHm7A1diYpmK3pSxkwuQSxWLeYVm54IgdgZfmBAKybvdjCbZQtfyR2BILnPCtLEIkoOxgOwG655RYIIfDLX/4SmzZtCnsbl8uF++67Dy+//DIkScLNN99seKBE8dDSpe2ElqOLW1LZhl4df/mifH424egd9RqSdMmAvX/HmcrPmbQGTF+CWKSa27TJgKnWgAnhD4521rRhZ03v11t2xyRJSramLwZgA4rClxWWFwQCsMBJhoZu9mKrC5MB63L7g2Yl2Lelx3uWiChWhj/NpkyZgttuuw1Hjx7FaaedhksvvRRtbf5ORffffz+uvvpqDBkyBI8++igA4Be/+AWOP/74+IyayKC2wGJu2bFF2gNe+cDRnYIATJ0Bi7S2Qk/OgNmZAYuKJkuTJmvA+hfmYHiZf6+qaAPvdNCVpiWIDtXeasWqDBjgPwFz7p8/xbl/XhbXphz+AEzOgPWdEkSXx/96HVQcfn23HHiVBTJhdd2VIIbJgHW6fXB6fJDPS3ANGBFli5jy+Y8//jgKCwvxyCOP4I033gDg31PpD3/4AwB/VyOLxYIHHngADzzwQOyjJYpRa5c/ABtQlIN3fnQ6vvhkkeZ6+aDcm4LFOOpjVqfbB7vFhK1HWjG0LE9zVl+NGzH3jjpIkDtepgM5+M6g+EtVgig34QjObSpKeGX6EkSr2QSLSYLHJ5S1SYA/GItX6a4kAXa5CUef2gfM/2+tGlqCmlYnNqj2MCzIsSjzK5fZNnRXghguA+byatbuMQAjomwRUwAmSRJ+85vf4Ac/+AFefPFFfPHFFzh8+DC8Xi8qKysxdepUXH/99Rg5cmS8xksUkzanvwSxX4EdRbnWkOstKVwDpj74dnq8WL6rGVc9vwLjBxfhrVvPCHsfNuHoHXWnvnRZAwYAphR23zRK35kux2rGtycMRFOnG4NLwpekJYO+BBHwj63N6VG68wFAl8t4oKRv5qMtQew7GTA5ACvMteLtW8/Aba+txTvrDwMA+uUHW8yWy2vAuilBrA0EZ1eeOgSvfX0AgP81Jr/ObGZTWu7dR0RkRFxWtA4bNgwPPfRQPB6KKKHkDFikjJKyD1gKDoTVe+o4PT68s8F/ILP+YDNe/GIPvjd1RMh95PUmXAMWHU0GLE1KEIHgFgRGmnB0uDywmEya/a+SQemCqOpM9/gVVUkdQzgOXRdEIBiAHVRlwDrcnpD7RktfIqxpwhFFBszt9eFnr2/E5BGl+M6kzO0OLH9m2QLvpTJVQ5lyVQBW6vD/3G0AFsiAnTioCP974yBc8exXmgCMW20QUTbhJxpllcNNnXh99cGIG4TKAZh+U16Z0gUxJQFY8DmdHh/6FQTXVfzynS3YXdsWch92QewddZlcujThAIINWHqbAWt3enD8gx9i+mNL4z+oHnSl6d5M+hJEIHjwri5B1HdE7Q2XJzQDprShj6IJxxtrDuL1NQfx09c3APCv/Yv0mZXOXIHPLPm9pO7oWV4Q/DnYBTHyGjC5C2J5vl15TXWqShDzbOyASETZI2GfaBs3bsTHH38Mk8mE8847D2PGjEnUUxEpLvrrFzja4kRjhws/ODO09FVuwpFvDy0/BFK7BsyjyYB5oa+QW7y1BiP75Wsuc7IEsVfUXRDTqQRRDvx7mwFbf7AJADSldcmiXwOWLvJVTTjkAEw+oD/Y2KFcF8u+YPogS9K0oe/5cXfVtis/CyFw5XNf4VBTJz6+6+y0m8/uuAPzIJcGlkTIgEXTBVHOgJXn25WsapcqA8Y9wIgomxg+BbxkyRKcc845uP/++0Ou+/Of/4yqqir85Cc/wV133YVx48bhqaeeimmgRNE42uL/Ev9ke03Y61sDbegjZcBSuQZMX4IoZ+tki7YeDenc1uFKzyxEulJnwFLZqU/PaACWymYXna70zL6qm6sU5/kP/OUxqgPVDlcMJYj6DJhJCjbhiCID1qgKRJo63FixpwEHGzux9UiL4TGlgr4EsUS1H9jA4uA6QDkz1qFrqqHW0un/bC7OswYzYO7g7dPtdUZEFAvDAdj//d//4dNPP8Xw4cM1l+/YsQP33nsvfD4fbDYbcnNz4fV68eMf/xhr166NdbxEEak3Wc2xhP+ybuuxBDF1a8Bc6hJEt08JFmVf72nAmAc+wLPLdgHw/3sbOvwHciWO8Bk90ioIBGBWswTJwMbXiSJ3QextFZpHlalN1CbDkSgH32m2/lBdxumwyQ1CQksQO2LKgGnvq2nCEcUasKOqjn87jsZ3T7Jkkl8DctA7rNShXPedicG1bfl2i/I6qY/Qil7+zLWZTUqw1en2BjdhZgaMiLKI4W/OL7/8EgAwZ84czeXPP/88vF4vzj77bNTV1aGxsRGXXnopfD4f/vrXv8Y2WqJuqBd4R1rf09MaMEtK14BpSxDlsf7qghMwprJAue73720D4C+nlM/ElznsoJ7JJYjp1IIeCG5B0Ns1YOp1g8nuoCgHf9Y0yiQC2gN1OciWD+jbVUFXbAFYN004oihBPNAQLIVUB2CxrEtLBflz0hr4t48bXIT/uXw8PrjzTM16MEmSlDLE+git6OXXk8UsKeWGQgDNnf7bM8tPRNnE8FFITU0NzGYzBg8erLn8gw8+gCRJePDBB+FwOGC1WvHwww8DAJYtWxbbaIm6oe5w1tzpDnub1mjXgKVgQXykEsSCHAtmHt8/5PbygYzDZub6iChVFuXAJGkbBKQDuQSxt1ksddCV7JMG8ia81jTLgJ0zpgIXjB+Ih+Ydr1wWrnwtlhLE0DVgkpLhiaYEUZ3d3q4KwPRZ73Qnv/7U6ykvqhqMMZWFIbeVG3FEWgcmv37NJkmzr2FDu39OWIJIRNnE8DdnQ0MDCgsLNWU8ra2t2Lx5MxwOB84++2zl8mOOOQY5OTk4ePCgoedatmwZ5s2bh4EDB0KSJLz55pua67/3ve9BkiTNf7Nnzw4Z79VXX43CwkIUFxdj/vz5aGsL7SpHmUt9Vrk2QretqNeApTwD5lON1YpzxlQo18nZMLmUpyyf2a9oVRTk4NUbTsML101K9VA0lBLEXgZg6tdMqjJg6dTMBPA3hHjyyip8X7Vtg5wVG1CUg9knVAKIrQlHaBfE4FYC0fwd1Jmu7dXqAMx4UJgK6qCpJ921ovf5BOSXvsXk3+/LFqhikJtzOOwMwIgoexgOwHJyctDc3Kw5Y/vll19CCIHJkyfDpCvxyc01vjFne3s7xo8fjwULFkS8zezZs3HkyBHlv9dee01z/dVXX43Nmzdj0aJFWLhwIZYtW4Ybb7zR8Jgo/agzYHURAjClC2I6rgHzCNXPPiVbV5BjwYQhxRhamgcg2EiiLpABk88sU3ROG1mGY/sX9HzDJDL14uBdTV2CmKzXbE1rF9qdnrRdAxbOJScPxqnDS/HMd09RmkO0x7UEUVKCkJ6CaJ9PKJ39AG0A1pIFGbBIyh2RW9GrX7tyFYK8bm9nYPuNAUWp29ybiCjeDLehHzVqFNatW4dPP/0U06ZNAwC88cYbkCQJZ5xxhua2LpcLzc3NGDp0qKHnmjNnTshaMz273Y7Kysqw123duhUffPABVq5ciYkTJwIAnnrqKcydOxePPfYYBg4caGhclF4OqFpMN3W44fb6QtaCKWV9ETZitpqNHQjHQ6Q1YAU5FkiShIfmHY/5L61S1pjIpTzqzU8pMxnvghh8zfiS8Jqtb3Pi1N8tRqnDhlOGlQBIv/V04Zw1uh/OGt0PAPDx1qMAgM5YShDdoU04LFF+duxXZeoBoEWV9ZJPEGUKOQsaXQYscgmiupmMHMzl2sxo6fJgRyBAHVLKAIyIsofhAOxb3/oW1q5di/nz5+P3v/89jhw5ghdffBEAcPHFF2tuu3btWvh8PsMBWDSWLl2KiooKlJSU4JxzzsFvf/tblJWVAQCWL1+O4uJiJfgCgHPPPRcmkwkrVqzARRddFPFx3W433O70PCspjytdx5dsB+rbNb+3dzo1m7ICwRLEXEv4+ZPgP3hyuj1Jn1eXavF+h9OtjDXH7B+jKTC2jYeaMfeJZdjf4M/4leRZkz5WvvaMiTRv8uvO7fb2ak67XG7Vzy64bYktB1y1tx6A/yDa6fYHCyb4kvY6iMfrzh4IlNqcxj/bO52hfz+f1//+9Xgjz0dNqxPTHvs04uM2d7gSNpeJeM/K+4DB1/NroDjQAKemtSvktl2q+fR5vXC7hdLJtrqlCwAwsNCeks8bftbFhvMXG86fMamct2if03AAdtddd+Gll17Cnj17cNVVVwHwLyC//PLLMW7cOM1t33rrrbCZsXiZPXs2Lr74YowYMQK7du3C/fffjzlz5mD58uUwm82orq5GRUWF5j4WiwWlpaWorq7u9rE/+ugj5OXlJWTc8bJo0aJUDyEtbD9oBhA8AH3/w4+Qp3uFN3f4b/P1l59hZ2DplHr+Dh4wATBh2/Zv8F7n9oSPWe3gYf9zA8DqDVvR5fb//PXnS7HJAuxqAQALfALYciRYtmRp2o/33tuX1LHK+NozRj9v9XX+v/3a9ethP7Iu6sdZe1gC4D9Q/WjRYhQneDng9qbg8+0+XAdAwuaNG5BbvT6xT6wTy+tuzxH/v2HXvoN47739hh7j69rgPABAZ2cnViz/EoAFbR2deO+998Leb2299n56W77Zg/fELkNjilY837Mdnf7P0+Vffo79ju5ve6jG/2/fvid03v19Nvwf1os+/AAmCXB3aT/Pd61fgZYdcRt6r/GzLjacv9hw/oxJxbx1dHT0fCPEEIAVFxfjyy+/xEMPPaRkmM4//3zcc889mtu5XC688MILEEJg+vTpRp+uW1dccYXy87hx43DSSSfhmGOOwdKlSzFjxoyYHnvWrFkoLAzt6JQO3G43Fi1ahJkzZ8Jq7dv7QPl8AvesXAwgWMoy7ZwZKHPY8MePvkFFoR1XThwM7/LFAIAL58xEjhkh87fu/e34rHofRow8BnNnHZvUf8PrdauBRn+GwVIyEDhYDYtJwiXz5kCSJKw/2IwnN69Qbv/t8QPw43NHaTY8TRa+9oyJNG9vNqzBlqY6nHjiOMydOLibR/CvP3J7fci3W7Bn6W5g304AwNnTp2NQgl8Ludtrga3+/Ry7JDsAFyadUoU5J4Yv/463eLzu2lcfxOt7t6C4rAJz555s6DFaVx0Edm5Rfs93OHDWmePx2MblsNnsmDt3mub2q/Y1Ysm2WlRNKMSLOzZEfNzifpWYO3eCoTH1JBHv2V+u/wRwuzHtrLNwbP/8bm+bs70Wr+1aC7OjGHPnnqa5rq7NCaz6FJIEnP+tuQCAfxxcgcMdzQAASQKuvHB2StYb8rMuNpy/2HD+jEnlvLW0tER1O8MBGAAMGjQIzz//fLe3sdlsPWaZ4m3kyJEoLy/Hzp07MWPGDFRWVqKmpkZzG4/Hg4aGhojrxmRWqzXtX/SZMMZEq2npgsvjg9kkQQgBnwCEZMaO2k489/leAIBQnU0tcuTC5/WXUKnnzxYoe+n0+PDGuiOYeXylZj+bRFKv61+yvRYAUOKwwWbzP78jRzuOIWUODOuX2pMDfO0Zo583i9n/upNM5m7nUwiB2U9+ii63D+/feabmNWMyWRL+t+hSLX2Su9nl2JL/GojldWe1+L/2BCTDj6HvNG82ScgJbG3hFQh53McX78KKPQ24cEL364073L6Ez2U837Pyeje7vefHrCj0nxxoaHeH3tbk/yy2mIJ/kzxb8PBkQGEOHLmp7fbKz7rYcP5iw/kzJhXzFu3zpf/qaQMOHjyI+vp6DBgwAABw+umno6mpCatXr1Zus2TJEvh8PkyePDlVw6Q4khtwVBbmKBt2ur0+TYexj7f4g/B8uyXionH58n8u34d7X9+IZ5ftTtiYF3yyE+f9zzKlK5i6CYe8Say6wYZdd/aX++JkD3OUbeidHh921bbjUFMnXl99EF2qZhDqRgaJ0q5qEiEPNdKm5+nKaMMTNZdXvw9YsJOlJ8wegnJzjVV7G7t93JYMa0MvB2DWKBqxlClt6J3YdKhZs22Ixxvazl698fLg0vReBkBE1FsxZcD09u3bp2SaKioqMGzYsLg8bltbG3bu3Kn8vmfPHqxbtw6lpaUoLS3Fr371K1xyySWorKzErl278NOf/hSjRo3CeeedBwAYO3YsZs+ejRtuuAHPPPMM3G43br31VlxxxRXsgJgl5Bb0Q0pzAwc7Xri9AjtqggHYoSb/bfIjdEAEQtspH2nujHDL2DR3uPHHD/1rzBZuOIKpo8qwMszBWUleMADTl9/kcfPlrKEEBaoOeu1OT0gTGXWHvX8u34cpx5SFvS5R2sN06cvUACyW+dLv12WSJOWzI9zDym3r5c+gyI+bWQvtlX3AzD03f5G3y+hy+3D+U58DAPY+8i0A4QO5HNXn21AGYESUZWL+5jxy5Ahuv/12VFRUYOTIkTjttNNw2mmnYeTIkaioqMCdd96JI0eOxPQcq1atQlVVFaqqqgD4G4BUVVXhwQcfhNlsxoYNG3DBBRdg9OjRmD9/Pk455RR89tlnsNuDJQuvvPIKxowZgxkzZmDu3Lk444wz8Oyzz8Y0LorN0u012FYdXa1sT+SzqYNL8pQDQrfXp7QwBoIHP5E2YQb8m7iqtXQm5oDow83BstxOtxe3v7ZO+f2MUeXKz6X56gyYNuDKZQYsa8j72csHov9ZeQAnPPQhXl6+V3M79X5Je+ra8dGWo2GvS5RwbdItURx8pxM5UxVLBuxwU1fIY5qVTdxDM2BOT3R7jmXaRsy92Qcsz2YOyeLLlHb25vAZsCElDMCIKLvElAH74osv8O1vfxsNDQ2aDZlldXV1eOqpp/Dqq6/izTffxJQpUww9z7Rp08I+vuzDDz/s8TFKS0vx6quvGnp+ir/dtW343j9WAgieBdU70tyJfvn2kKAonH31/gBsSEkebIEvcbfXhx1H20JuG2kTZiBYViX7ZHstNhxswkmDi3scQ29sPNSs/Fzd3IUtR4KB6Jxxlfh8Zx0AbQmiPgOWywxY1tCXxf30dX+jhgfe2oxrTh+u3E6/11dta3BTW2bAohPMNhp/jMO6TJYkdZ9Zc+kXjQXkWs2aTZnbMigAE0IEM2BRBGCSJKE8367JAgohIEmS8jiWCCWI3AOMiLKN4W/OmpoaXHDBBaivr0dBQQF++tOfYtGiRdi6dSu2bt2KRYsW4d5770VRURHq6upwwQUXhDTCoL7raEvwwDHcQd17G49gyiNL8JuFW0Ku0+tye7EosLnqSUOKYA0EKs2d7rAlPwU5kRdIVreE3v6Cv3wR8QDKqB1Hg5m5oy1dsKkOYkf1y8fIfv6ezuoSRP3ZY2bAsoc5yqyMOssl6Y55kxKAuUIzOdYMzYD1tN6uO4d1pcnqEsRwfwdnhM8PfYOfTrdXsxY0nan/mdFkwIDQf688L2HXgNnUARgzYESUXQwHYH/605/Q2NiIMWPGYPPmzXjkkUcwY8YMHHfccTjuuOMwY8YMPPzww9i0aRPGjBmDxsZG/PnPf47n2CmD5ViDLz19kNTm9OBX72yGEMA2VQlhJCv2NKCpw43KwhycdWw/5WBgy+Hw5Y0F3awB++5pw1BZmIPrTteuX/x0R22P44iWEEITgB1p7tIceLS7PJh/xgjYLSacNTpYjmgzMwOWrSQ5KOjh2Ftd8jVtdD/NdR6fgBAi7icL1LIpA2Y0YPX5BI7oSxBN6ixmaKbS6Q7/NynKDT0ZlClZMHWpZTQZMCC4Dkwmz0vwda1aA8YSRCLKYoa/Od99911IkoTnnnsOgwYNini7gQMH4rnnnoMQAgsXLjT6dJRl3N7gAYq6GxYAPLX4GyVDpu7yFom8cH1oWR7MJkk5INwcCMBOGKht1d7dGrATBhbhq/tn4HtTR2guX7W3ocdxRKupw43GjuDasi2HW5QypHGDijDlmHJcPXkYtv1mNk4ZVqrczmSSNEEYM2DZQ/6z9pQBk7M2ZpOEm84+RnudT+CO/12HU3//sX9fpQQIH4BlVgYs2rmOpK7dGdIF0Z8BC7439dm1cGvALq4aFLaRTqasA1MHsJYouiACoSeR5HmRgzlLmDVgNosJFQWpbUFPRBRvhgOwvXv3wuFwYOrUqT3edurUqXA4HNi3b5/Rp6Msoy6zUQdgBxo68PfP9yi/d0U4c6wmn/GXS/TktVLyuqpThpVoyrW664Io0wdp0QSC0VI3Mih12JSDucEluXjntjOUM7+SvsYM2jJEtqHPHuG6IIbjVZVqnTayDLedMyp4nU/g7fWH0dThxv9+vT8h4wzXhCPTMmBKCaLBDJjcgEOdxRfCnwWTqR/b4/WFdEY874T++N1F4zRZ7PJAdqjVmRmdENXlsNFmwGpatScGui1BDMzv4JJcmKJ8fCKiTBHXNvQ96a6RBvUt6jPI6hLEDQeb4fEJ2MwmuLw+zQL1iI+lC8DkA8KdNf4GHMdVFqDAblH22CnO63mTPH0AFmkNhxFyMFeSZ8XSe6Zh3f4m7DjaiknDS3u4ZyC4DBzDsA199oh2XZLSLS5wQHr3rOOwaMtRbKtu1Rz0y5skx1tHmDVg0TTJSSexliDKDTiGlTqwPVBK7PUJbQZM9djhPjvmjhuAXJtZcxKlMNeKujZX5mTAvOoMWHQB0tDSPKw70KT8LmfAwrWhLw6sfx1Z7oh1qEREacfwN+fw4cPR3t6Or776qsfbLl++HO3t7Rg+fLjRp6Ms41YdlKjPqsslTvJagagCsEAwJ2e+9AcDo/sXKF/mAFDq6LmcRd/yPZ4BmPxvyrGaUZhjxVmj++EHZ47E+CHFUYxLVYLIACxrKK3Re8qAhekWF679eUOCArBwmeCMK0GMsQ29HIANLQuuS/IJocneeCIEYPL712Hzn+BRlxHLa1PlAOxPH23HpN99HFKinS7kf6MkIeoM1X1zx+DbE4J7b8oVDu4w3RRnndAfP5k1GvfOHhOvIRMRpQ3DAdicOXMghMCNN96I2trIDQpqampw4403QpIkzJ071+jTUZZRZ8A6VWfV213aAKwrzBl3PXkht7y+QN+ufXRFAfqp1hDoO3FFI9p9fKIh/3uNrOFSlyVyDVj2ULIyUa8BC77Gw3XfS1QAFu5EhH5dT7ozxZgBkzP26s2B9QGY+rHlDL3V7C8bBYDhgayOJgALdGdt7XLD5fHhqSU7UdvqxL++Ss/S/d7sASYbUJSLx6+owrBA8Cq/nrxh1oDl2Sy49ZxjcWz/gngNmYgobRguQfzJT36Cv//979i8eTPGjh2Lm2++GTNmzFAachw8eBCLFy/G3/72N9TX16O4uBh333133AZOmU29Bkxd1iRnwMrz/QGTkQyYek2Kw2ZGUZ4VlYU5ymWGArAo1qJFS50B6y11GS8zYNkjuDlw97cLrpUJXiYf+KvXSyYqAAvXYTFTSxCNdu0/HCYA8/oE1HGItgTR/363W8xYcPXJqGt1BgMwmzoAC2bAVuypVy5vSdOSRPkzPNr1X2pyJlBpwhFmDRgRUTYzHID1798f//3vf3HRRRehoaEBv//97/H73/8+5HZCCBQXF+PNN99E//79YxosZQ+3J3iAog6y5H2GygJlgh6fgNvr63ahv3wWNVwJonxWub+BAOyjH5+FR97fhiXbahKyBsxIAKU+aMy0zANFJr9koy9BDP7t5YNWOXsM+DttJoIcgOXZzMqJk0wrQYxXE45BxcHNgX3Cn522mPybCodbA2azmJBvt2iaAH3rpAF48cu9GFCUg8LAZ1VLpxu7a9uV2+ys6XkrjlQI91qMllzi/e6GI7j+xZWYe+IAANo1YERE2SymT7uzzz4bGzZswE033YSSkhIIITT/lZSU4Oabb8bGjRtx1llnxWvMlAUiliAqGbBgkNRTB8JgEw7/l7pVVYKYHzirrN5/pizKAGx0/wJcdspgAHEuQXQbL0E87wT/SYyBRTlhuyRSZorUGEKfEVC3odffRt0iPlENj+RgorIoeEIj0w6a49WEY6AqAJMfyxRmPZ6+SZDapOGlWHjbGXj/jjNRFGgO1Nzp1oxtW3VrWjaw8oRZtxUteS5eWbEfXW4f3lh7yPBjERFlopi7IA4ePBhPP/00nn76aezZswc1NTUAgIqKCowYMaKHe1NfpS5B1GTAnP6fi/NskCR/e+dOt1fJZIXj0mXA1JkhuaxHfVm4zU8jsVvlUpl4ZsD8j2WkBPGXF5yAueMGhGxoSpnNFKEszqwLssOtu5EzEOpS3p7Wkhklv9cGFOVgd207zCYp41qEx9KEo8vtVTpMajNgwb+LC4Aq/lKVIIYPVE8cVAQg+LnU3OnWBLitXR7sre/AiDTrBmhkDZjMbg0/F5YMy6YSERkV1zb0I0aMYNBFUVGvJQmXAcu3m5Fr9Zc5dbm6D35cXv/95SBLXRIlB25FqtbzvTlglLNqcV0D5jJegihJEiYHFvJT9ogUFOgzAvJaGfVr2BQmA2Y0u9MdIYSSuR5Q5A8+jBx8p5qcsDMyR3L2y2EzozA3+PXp02WD1BkwpUlQhABMVqgKwDy6sa3d35h2AZh+S4Te0HeZlTEDRkR9RWbVjlDWiJgBC6xjcdgtSomefL3PJ3D3f9bj2WW7NI+lP8CxhMmAXTB+IE4dUYrbZxzbq3HqF4vHQ7AEkW8/8pOPO3ssQQybAfP/rM6A6Q/g40GdBR4YyNBk4jrEYBMOIwGYf/3XwOJcTQmwV2j/Lpo1YF5tiXQk6gyY/nWg3jsrXSh7dxl4DUTKBhpZT0ZElImSuhEzkcyl2sQzXBfEPJtFKdGT14BtONSM19ccBABcN2W4ckDj8mrXWGhKEAML3nOsZvznptN7PU4lA5aIJhxsI08BSmt0XVCgTwh0twZMvZ+eepPceFGv26yUM2AZWDJmjqEJR7j1X/7HCjy2kgFTBWDuyGvA1NQBmJxRK7Bb0Or0YO3+pl6PNdHisQZMLxMzqkRERkR1uslsNsflP4uF8R75RcqAycGYw25GTiBDJF+vPmB6+L1tys/6Re7aEsTYXnOJWAMmlyAaWQNG2UkOCl5dsR+/fmeLcrm+xbs3TNlXMAMWDMDilQFr7nTjy5118PmEpmy4ssjfpdRI9iPVYtkHrKXL312yOE+7jtSnC4zDtaHvsQRR1YZeDrRPGV4CANh6pKXHZkTJFtMasEgliBkY0BMRGRHVt6e+u2Es/xEB2jVgLo9P+TLXlCDatCWI6rViL365F5sPN2seK9w+YN0174iGUoKoOvjxeH14d8MRHGnuNPSYsewDRtlJvabrH1/uCV6ua8IRbr+k4BowVROOOAVg17+4Elc9vwJvrD0UfJ+ZTcoeWCV5mdcMxhzlnmvhuL3hy+6CTTj8l4fbiNlIBmxoaR7K8+3w+AQ2HWru/YATKJa9u9RNONSBqZUZMCLqI6JKD3zyySeJHgf1MeoMGOAPSvLtFuUg0mELrgHrCgRe6n2OAGB/fQdOGFgUshGzOmug3nPHiHAliHf+ex0WbjiCc8ZU4IXvTer1Y3bGsA8YZSd1oKU+T6VPMPlEaNYhfAYsPhnb1fsaAQD/XrkfpwzzZ2PsFhNGVRTgL1dVpV1jiGjE0obeE/is0e99FmxDH7hdhH3AuiMHYG1Oj3IfkyRhwpBifLz1KNYdaMLE4aW9HnOieONQgphjNWHcoCKs3NsYeKzMy6gSERkR1dHp2WefnehxUB8TEoC5/AFYm7IGzKxkiOSyxA5dAFbX5gSgasJhNgf+H8cSxMCBgscn4PH64BPAwg1HAABLttUYekyuASO9SJV8+jb04dbdBNeABTNgPuFvWhNLi3h1xYIkSSGZ5vNPGmj4sVMp0nq7aLiVAEyXAdNtSqxu8CEHbfpyUr1C1fYY8kbaZpOEqqH+ACzd1oHJQb6RdYDyZ/uJA4s0VQpcA0ZEfQVPN1FKuDzag59Olxddbq9ykFeYa0WergRR3awDAGrb/PvxOL2RSxDLC+wxjVNdKuPy+tAQ2ANI1tTh0t+lR/I+YAzASKYvNZTp18SEyzqEy4ABse8F1tIZfDwJoaW+mUopQTSQAXNH6Pynb47iUTVBkX+K9DeWWc0mOAKfeQ2BzxWLSULVkGIAwPqDTb0ebyIFX4u9fz3ImdNpx/XTVAJwDRgR9RW9+uT0eDxoaWlBS0tL1PeRb+/1ptcCYkqtcCWIrV3+Az5J8nf/ctj82Su5M2KHUxeAtfozYPo1Fo2Bs8cAcPLQkpjGqe6o6HT7lKybbGdNW68fs7nTPz5HjOWRlD0iBmAR14AFX5fmMGvAgNjXgR1s6lB+bmh3Rd1MIt0p+4CpAtQ9de1YtOVoj/d1e8JnfeSKz3Bt6OUfo0nuyGWIjYETPSaTpGzKLH9upAtPDE04LqoahE9+Mg03TxulORHFNWBE1Ff06pv0iiuuQElJCb73ve9FfZ/rr7++1/eh7OfSdRVsd3nQGugwlm+zwGSSlAClXbcGTD4mDQZg2gPD5s5gVqooN7YmHBazSTnAcHp8qNdlwHYc7X0AtreuHQAwrCwvprFR9tCvKZLpSwj1+00BwQBMnwGLtRPiwcZgk5lDTZ1RN5NId3JQK0SwzHL6Y0txwz9XYfW+hm7vK5840u9/JpccyoG0OrgTuuu6I5chNgYyYGZJUrJt+pNWqRbLGjBJkjCi3AGzSVIqHfyPldmvLSKiaEX9abd582a88cYbKCwsxAsvvBD1Ezz33HMoLCzEa6+9hm+++cbQICn76A8mmjvdePxj/+tDPgjJswfWgMkZsEAgNizQgW13XRs2HWpWSvrkAOzGs47BCQML8cQVE+IyVvVmzHWt2gzYNzWtvXqs5g63EsQNz8AGBpQYkdq567MLSrMHSV2C6L+vvkQ31r3A5Iy0/Ng1gdd+pmfA1AGD1yeUEyIA8E0PJ1TcurVeyuPIgbFZzoAFP9/k4CyK+Ev57GtQZcDk+fYkYG+3WMSSAVNTZ8AycV85IiIjov4mfeWVVwAAt9xyC4qLi6N+gpKSEtx2223w+Xz417/+1esBUnZy6QKw/7fqIN5efxhAsHGGUoIoZ8ACgdioigIAwO7adpz/1Oc4FNgcVT4rPaLcgXdvPxMXThgUl7HarcFOiPXtsZUg7qn3H+z1L7TH3KGRskekBg0hbejDHPRGykDE2glRn6XeHQhU9NmfTKPOKnqFwGc765Tfe9prSy5BtFq0cy4nvMKtAZMTkRJ6Di6UEkS5CYckKX9rj08YWreWKHJzESMZMDX1dhxswkFEfUXU36SfffYZJEnCJZdc0usnufjiiwEAS5cu7fV9KTvpM2BbjwTXFRYGumLJpSkHGzuwYne9EoCdOqIE984eg1N1LZkTVRolj6O1y4P6QOMPuSV3T2fM9fbU+W+fie27KXEilSBKugBMPgBXNyuIdAAc6xow/Xv0vY3+7p+RNtHNFOp1dT4fNFnturbum+pEKkHUP7Z2DZictex5bPqSabMJsKo+19xx2l7AqLfXH8aFf/kcBxo64pYBU5cgMgAjor4i6iPWHTt2wGQyoaqqqtdPctJJJ8FkMmHbtm29vi9lJ3lDU/mAQ1OCFfgOljNEn31Th8uf/QofbvYvknfYLbh52jH41w8max4zURsblzr8m802truUA7TJI/zBX3VLF1q6ol8cv7vWn0UYUZ4f51FSJotUgqjfvL6nDFiBKqsa6xowfQZMzvZmepmYWZcBUwea+gy3nruHoEPZY0yzBsz//2jWgIUGYCZNsOdOcRniG2sOYv3BZizZVhNTF0Q1dRdEOzvDElEfEfUnZ1NTE4qLi0POyEb1JCYTiouL0dzc3Ov7UnaSD+7K8v3BjXqTZTnTlacr0ZPb0culifq1KAOLcxMyVjkAa2h3KY0OBhTloLLQ352sN1kwuYxrJDNgpBIpANNnseS1RWbNGrDgzzNP6K9kFGLNgOnLhGXySYRMpQ6EvD6hCVRrW3vIgCkliBHW7JlDM2BKE44ovm3DZsDUAZgntRkwuRNtfZszIWvA2JiIiPqKqAOwvLw8tLb2ruGAWltbG3JzE3OATJlHXmtREdinS93eXd6M2WELfzZUXbJyXH//erAJQ4pjXosQiRyA1be7lHHbrWYc29+fxdrZi0YcewIHryP7MQCjIHVWSX2OyxchA6Z+ratf9eefNEC5LtaueZHuL6+5zFTqufP5hCbT11MGzBNhH7DgY4c2zFDWgEXTBVG3cbxJkmA2SUr5ovpvsru2DesONPX4mPHU4fZ/Nte1u+CV14DFmBFVZ8CGl/FzkYj6hqgDsIqKCrjdbuzatavXT7Jr1y64XC5UVFT0+r6UneRF5sNK/V+4cidDQJUBswUPRr572lAlaFF3D1xwdRWumjwUz107MWFjLVMyYE5lnDlWM44NNAOJNgMmhMCeOrkEkQcaFKQuMzthYKHysz6JJa8BUwdsa/Y3KT+fMapf2L2ojJADk4GBfahk+rWXmUZ9nkZfgqjf509Pvm2kNXvyxeoSxF6tAcvTZ8CkwPP5Xx/qrOQ5f/oU317wBWpau3p+4DjpDDREqmuNXwZM/TplBoyI+oqoA7DTTjsNAPDGG2/0+klef/11AMDkyZN7uCX1BUIINAX2uRlWHvqFe+/sMQAAhz14ZnRMZSHevvUMvHf7mRgdyHoB/o6Iv79oHPoFMmmJUOrwP3Z9uwtdgT3HciwmjAgEhPsaOiLeV626pQudbi8sJglDSnmgQUHqjEq/fDteuv5UAJEzYOoyugsmDAQAnDqiFDaLKZiFiVMTjmG6rMTjcdreIVUkKZhR8vm0AVinq/suiHJQ2lMGLNxGzL3pghh8PP995ABdzqyp93w70pT8AKy+3aVse5Ab47otp+rkW0FObPs2EhFliqgDsPPPPx9CCPzxj3/EkSNHon6Cw4cP47HHHoMkSTj//PMNDZKyQ5vTgz98sA0bDjYrB4dyBkw2fnARLps4BACUjZgBYEhpHvLtFhyvyg4kS5lqDZg6A5anak8fDbn8cGhpXsQDOOqb1Bkti9mklKKFrgELzTpcXDUI/5o/GS/PP1VzXbwyYOqM83dPG5qwtZbJpG6WoW5s0VPQGsz6dL9vm+ZxYuqC6L+T/PqQg8V6VbfGRJVeh9PhDq4BawpUMRTn2WJ6zG+dNAAnDy3G3TNHxzw+IqJMEfVR4CWXXIJjjz0W9fX1OO+886IqRdy5cydmz56Nuro6jBo1CpdddllMg6XM9s76w3h66S7c+PIqAP628f0LtZmrY/oFuwOq13oNKUndQZ+6CYczcACSYzUrC/GjXRi/m+WHFIG6BNFikpQMl37fp3Cd5yxmE844tlxpD28OFwQY4AoEJgNUJYhuT/rsQxULk6pdvLqsr6fNq5U29JYIJYhyYKd6zN6sAQsJwKTwJYjqUsme9i6LJznrVdfmQlOnPwgszosta+WwW/DGLVNx24xjYx4fEVGmiDoAM5lMeOmll2Cz2bB582acdNJJuOmmm/D++++juroaLpcLLpcL1dXVeP/993HjjTdiwoQJ2LRpE+x2O1588UVDHRQpezQGyg6PtvgPHkodtpADDnUmoFBVjpLKs+6lgU6N9W3BJhw5VhNsurPSPQm2oGcARlr6DJh8IK+Pobxh1oBFeixvjHtGBYON4NeE05O8g/1EUubXpz2B0lPQKmfLIpcgypm14GXBNWBRNOHQfR6adGvA5OdXZ8DU62cTyatqWNLm9OBoi7/0sTiXZYNERL1l6fkmQaeddhr+85//4JprrkFLSwuef/55PP/88xFvL4RAfn4+Xn75ZZx++ukxD5Yym9zCWFacFxqAqQ9sHHYLXr/5dFhMpoTt8RUNdQmi3LErx2pWHRRFdwC0P7BWbBgDMNJRv+6tqgyYN4o1YHpKBizGPaPU650mjyjFij0N+M6kITE9ZrowS+oSRFUGrMcAzH9buQSxzGFDfbsLOVZT4PLQ4Fd+yGgqBQtzwmfA5CBYKUFUdWvsTFIGTP88u2r8J5RizYAREfVFvV6IMm/ePKxatQqXXXYZJEmCECLsf5Ik4bLLLsPq1atx4YUXJmLslEH21rXjb8u0ZasledaQM776M8unDCvF+CHFiR5et+QSxE63V8ni5VjMykFRtGvAGgIHTf3yE9cwhDKTVVNSKCl7Ruk3Yg63BkwvXmvA1Bmwl64/FR/fdTamHFMe02OmCzl+9fr0a8C6fy/rSxBfveE0nDOmAv/vh1MAhC//DO4D1nMElmM1w67KOAa7IAay7R65BDGYAUtWAKZu/AH4mwoBQFFubGvAiIj6ol5lwGSjRo3Cv//9b9TU1OCTTz7B5s2bUV9fDwAoKyvDCSecgOnTp7PtPClmPb5Mc6ADACV5NuRY/YGMfLZdv7lyOsi3W2Azm+Dy+uT19MixmnqdAZNb78sBHZHMatGVIErhg6jgGrDuMmDx7YJoM0vIsZoxqiK/h3tkjmCJp3YNmE/4191FCpY8uhLE4yoL8ML3JoU+rqYLov/naAvwi3KtqGn1n6zRlyCmcg1Ylyv85xwzYEREvWcoAJNVVFTg8ssvj9dYKIupNzs9a3Q/rNhdjzOP9Z9NL8yxKgcUkfbXSSVJklDqsClnfAH/Rsz6dRk9qW+T177xgIW01F31LCZJWS+rj6Gi2XspXhkwZw8t1zOZWTVH+hMoHp+ALcL8unQliJEeV5sB8/8/2jXQ6gDMogvA5ABQvh5IXgAmb8KsxwCMiKj3YgrAiIyYe2Il/vG9ScrBSlGuRRWApefBnj4Ay7GalFIhVxQliG6vDy1dnsBjsQSRtGy61324TAoQXFvUXTlbvLoghmvCkS3UXRD1AVh3gaunhy6I4YLf3qwBA7SdEOVx2nTZ9urm4GdRT3uXxUtHhOcpZgkiEVGvZd83K6W9PLtFU0KlPuBI1wCsLD94kCFJ/gOi3pQgynvmSFJoq2kiq+6A3hyhCYf8UusuA2aNUxfEnjYdzmTy548Qoa31u1sHJme7I2XATGEDsOi7IAJAfk7wvKh+HzBXmAAsWV0QwwV6NotJaUBCRETR4ycnJZ3Dpu1oqG7Eoc8EpAv1uq0cixmSJCkHuq4oAjC5eUdxrjWpG6dSZlAf0AsRbBLhCwnA/K+17teAxSsD5r9/ur4nY2GK0AURiJwBE0KgzenPYlsjZAXDZcBELzZiBrTzLf+oLnf2+oTSAh5IYhfEMAFYca6V28sQERmQfd+slPZydQGYNgOWnl/mmgAscMbX1osSRHnfnhI24KAw1K97AaHZp0rNE0UTDjmYi2cXxGyjXgOmP4ESLnAVQuDu/1uv/B7pcypcAxTlxygDFfV8m3QbMbu9PtS3OTWPn7w1YGECMK7/IiIyJPu+WSntOWzapYfqAMySpmfbyzQBmD+A1K/L6E5TIANWkscAjEKpswhCBA+8QzNgPTfhSMQ+YNlG3QUxmgzYpztq8caaQ8rv1ohNOEIfw9fLDJjdEjxBJQfT8pozt9eHI6ryQyB5AVhnoA29OkPH9V9ERMZk3zcrpT2HXVeCmJMJJYjBxhlyACYfmPpEz9kGuUwoz5a6DaUpc8jH9/o1YHKw0F1QFK8uiC7ludIzKx0LORjS7wMGhM+A7alr1/weqQTRHCb7KP8U7RowTQZMV4Lo8oQGYMnbB8z/PINLcpXLipgBIyIyJD2Pdimr5XaTAdM3I0gX6hJEufuh+kCppyyY3NJbfXabKByBYBMOIbSbMTuj2C8vXmvA0nlvvlipu0y6dSXE3jCZw5ZObQv2SBlIS9g29L3NgKnWgOlKED0+gSPNnZrbJ6sLohzoDS7NUy4rZkMhIiJDsu+bldKOPjjRN+HItC6I8roH9ViduoO4177ej5+9vkFpW+0MHLzYs/BgluJLXYIIaPcCiyYossSpC2JwI+bse82qm3Do14C5w8xbS5db83ukOQmuLVNt7hz4MdpmFeq/rVm3D5jb41M6IJYEPoe6oliDGg+dYTJgXANGRGQM9wGjhNOvUdA34SjMDb4M0zUAU2fARvcvAKAtzdIHmfe9sREAcPKwEnxn4hDlII8BGPVMaPb58voEOlwerNrbqJSBdRcUhWsEYYTSBTELX7PdbcQcrnSzpdMfgJ04qBB3zBgdcR+24OMGL+ttG3r131Z+Hps5dA3Y8HIHGvc3KWuzEk1+7RXkWFCcZ0VTh5tbahARGZR936yUdtTZoYfmHR9ShpcJbejVTTiGlzkAQNOKXn0Qpy4ZW7u/CQDgDOzVY+eeORQF9fG9Twjc9PJqfP/Fldh4qBlA5DVIQBzXgPWBJhxur1AyjPJ7WW5eUtfmxP+tOoBOl1fJgF0xaShmHt8/4uNawmXAAo8fbbd2W5gSRLk5kcsrlAzY0EApoH4NW6Io61itFuXzsIhNhYiIDGEGjBKuS1V+9/2pI0KuVzfhSNeDPfUYBxbnKD/bzCa4vV7UtDgxoMhfmqNeFP/5zlp8ubNOuSxdA0xKH0Jo28z7hMCXu+o1t7F3mwGLfQ2YUJXmpet7MhZyNkqdnc+xmuH2epTA9arnvsKOo23YcLAZzYEMWGEPGZ9wcy8QwxowfQmi14cjLf41YIOK/Z83+k6ZiSKXIObZzBhckoddte2oLMzp4V5ERBQOAzBKuGADivAHcpmwD5jJJOGiqkH4pqYV046rUC6X/20XLvgCex6eC0mSlIM1ADjQ0Imrnl+h/G63sgkH9SzSGjBZt2vA4pAB63IHMzg5WZi1lQMbdQCWazWjtcsDTyB7teNoGwDgvY1H0D8QaPRUcqduby+TfzTSBVF+PPmyLrcXR5udAIDBJf4MWKyZzmh1BEodc2xmPHD+8Zixqw7TjuuXlOcmIso2DMAo4eSDnJwIwYe6lXG67gMGAP9z+YSQy9Rnui/4yxf41/zJmo5p3xo3AO9uPKL8zjVg1BN9E45wB9jdZaXisQ+YXHJnkoB8e/Z9TcilfeoGFvLnk36+JSk4H4U53c9FuLmXg7FYmnDI21e8smK/MqYBgUx88gIwuQTRjFEV+RhVkZ+U5yUiykY8GqSEk7NEkQKwfJtFWR+RySV6Gw8149nPdikZsBHlDtw87RjNbTL530fJ4bBbtCWIYQ6wo8uAGe+O16IquYs2cMgk8v5acndSiym4njNc6WZLlCWI4bKPPiUDFt3YNE04JG0AJivPtysnc5IRgL217hA++6Yu7FiIiKj3eDRICdfVQwt2k0lCSWAxd64ts1+SzZ1uzXoRfckSm3BQJI9cPA4Th5XgtnNGhTTh0Ot+H7DYuyAGMz7Z2eXOEpgjeV2T1WxSLtMHNF6fQKvTn9XuaT7CzX2vuyCGzYBpM29CCCWLp9+sOxHu+N91ys85DMCIiGKWfbUllHbkDoCRMmAAcN+cMdhe3Ypj+mV2WYuE4BqwolxryBlzbsRMkVxx6lBccepQ5XdJ8pcjhjvA7i6TGtwHLIYALFBGq94iIpvIgY38XlVnHfWBa5vTo6zj6mk+5D+Ldg2YXIIY3djUnxEWU/gMWEunR9NKP5nyuI6ViChm2fntSmnF6el5E+LLJg5J1nASTh2AFdgtyoE0kJ17KlFimCUJHiEQLsHRfQYs9i6I2Z4Bk8sNG9pdAPyBVaQNrOU27zlWU48nUJQMmFcdgPn/H20pp/pzUt4HTL93YonDmroAzMbDBiKiWPFokBKuK4oMWLZweryqAMwCk0nSHMSyCQdFSy5ZC3eA3W0GLA4H5sqapywNwORyw/pAAFaQY9XsDRZONHMRfg1Y79rQh9sHTJ91evKKqmDHxSQHYPpgkIiIeo9Hg5Rwh5r8+9aU5Wf/pp1NHW40Bg7q5HVt6nVgzIBRtORGEXL7b7XoMmAxNOHoyu4SRHkjayUDlmOBNcIaMFlPDTgAVdAswjXh6P0aMPk1oM46XTFpCCaPLAv7XMmQrluFEBFlEh4NUsLtrPHvpzO6f0GKRxJ/T15Zpfm9qcONmtYuAEBFgR2ANgDjGjCKlnyA3doVGoB114Y+2gxYU4cL7288gnc3HMGmQ82a67I9A2Y16UoQVRmwSKWbPbWgB4Jzr9mIuZcZMPXfVs6AqbNOjsC2AMkqQRRCKP8uAMqeaEREZFx2nt6ktLLjaCsA4Ngs3DfmgvED8Yf3tylZvqZOl5J56FfgP1BRZxFYgkjRMncbgEU+mg+3DimcO/53HT7dUav8/tlPp2NIqX9zX2UNWBRZn0wkr/dq7JBLEC3KvzlS+/6eNmEGAHOYdWS+Xq4BUwc7+n3AAMAR+DkepabR6HL7lIDys59O7xOl5EREicajQUoor08oGbBjszADBvgP3mSNHW7UtDoBAP3CZMBYgkjRkhswtDm1AZjNYur2YD5cFiacg40dmt+PNHcpPze2BxvJZCN5w3d5c+HCXGuPG1hHE4wGg6LgZcpGzFGPrfsAzB4IgExJCsBaVZtyDy7JTehzERH1FTwapIQ61NgJp8cHm8WEoYGz69nmscvGKwdKTR0uJQCTSxArC4MHLcyAUbTkREibLgNm72Ez72i7ILq82kyPR/X7wSZ/cDaoODsPuK26esACu6XHjFI05ZjK3lyqDJjo5RqwcBkwdQmi/DjycyW6B4ecGcy3W7JyU24iolTg0SAllFx+eEy/fOVgItucOKgIG385C4C/g5rLI5cg+gOw4eXBwJNrwCha8vulVZcBs/YQxEdqp67n9miP3N2qI/n99f4AiqCyBwAARQlJREFUbEiWnjSx6ILYgpzI+4DJomlIEu4xlC6IUX7bmlU3lIMtdddLOQaKR7OVaAQbsmRnNpSIKBUYgFFCfSOXH2bh+i+1XKtZU15YkGNR1koML3Mol7MEkaIlKWvA3JrLu2tBD6DHUjqZnAHLDbxO3YETB82dbuWgO1tLziy6NXSFuValNX2kDFg05ZjhNsHubQasNC/YLVb+W6ozT5LuugTHX0pDloIsbchCRJQKbMJBCfVNjT8DNrp/dgdgkiShONeqlB8W5wUPVjQBWA8Hz0SySE04egrio23OIGdqHXYzOt1eJZNyoMGf/SrPtykd97KN1aTPgEXTBbEXbejDZMCiLd8ryrPi5fmnwmo2he12qZQgyn/nBLehl/c1LGYGjIgobng0SAn1zVF/BmxURXY24FArUZ25zrMGD1wHFgfbNudH0cqaCAgeYOvXgPW0D5PSBTHKAEzeY0regFhuzjG4JDvLD4EwGbAc9Rqw8Cml6JpwhGbRetuEAwDOPLYfThtZprlsWJn/73Hu8f0BaIM9kcAgTG7VX+rI/n0ciYiShUeDlDA+TQfE7M6AAdqsl3rRvMVswhu3TEFbl4cHMRQ1OQCTMxD6yyOJJgMmhFBKEOUsVzAD5t9SIVvXfwGh+6ipM2DuSF0Qo2nCEXYNmP//0ZYgRvLBHWehscOFgYHGKOrXgU8AidofWdlY3sEMGBFRvDAAo4Q51NSJTrcXNrMJw7L4YE6mDsDUbaMB4OShJckeDmU4OdOlD8B6SnZE05xB3QFR3ldKDjz2B0oQh2Tp+i9A22kQ8DfYCLd+S603a8B86sdQAjADA1XJtZmRawv+TdQBmNcnEtbkqF7JgNkT8vhERH0RAzBKGHn918h+jpCuY9lIU4JoY7dDio2cpWnSBWB2a+xrwOTyQwDIs8sliIEMWKAEMVu3jQDCdUEMZsA++6YWS7fXhNwnmi6IcpYrXBfEeLdw12bAEleCKG9WXZrHDBgRUbxkxFHxsmXLMG/ePAwcOBCSJOHNN9+MeNsf/vCHkCQJjz/+uObyhoYGXH311SgsLERxcTHmz5+Ptra2xA68jwuu/8r+8kMAKFYFYLk2ntug2MjNNpoDB8Cya08f3u39otkHTBOABbogyl0T5SYc2VyCaNPV6+XbLcr6rZV7G7Fmf1PIfaIpQQwX/Cpt6OOcoDJL2gxYojQoJYgsnyYiipeMCMDa29sxfvx4LFiwoNvb/fe//8VXX32FgQMHhlx39dVXY/PmzVi0aBEWLlyIZcuW4cYbb0zUkAnAjkAANrp/9jfgAHQliFZmwCg2Nl0G7KTBRfjFt8bislMGd3s/OXPWbQYskO2ymCQl0HN7fRBC4GBjYA1YVjfhUG0ZYffvAaYvS9QriKKBjjlsAOb/f6xrwPTUjRx7argSi8Z2/+uP61eJiOInI07Tz5kzB3PmzOn2NocOHcJtt92GDz/8EN/61rc0123duhUffPABVq5ciYkTJwIAnnrqKcydOxePPfZY2IBN5na74Xa7I16fSvK40nV83xxtAQCMKMtNyzHGe/4K7cEjIrtFSst/c7yk+2svXfVm3uRu8x0uLwDg0pMH4spJQ+DxeLq5FyCE//Zujy/i87R3+bMaVrMEORZxuj043NgOp8cHkwSUO8xp9/eN1+tOEsEMYH6OBW63G92dM3HYzRA+L9w+b7ePKwLXe3zBufcF1uJ5fd64zqdQBV1OlwvuHr7Njc5dQ7t/a41CuyntXg/JwM+62HD+YsP5MyaV8xbtc2ZEANYTn8+Ha665Bvfccw9OOOGEkOuXL1+O4uJiJfgCgHPPPRcmkwkrVqzARRddFPGxP/roI+TlpfeZ4EWLFqV6CGHtqDYDkHB462q8ty/Vo4ksXvO3q0EC4D+KO3xgL957b3dcHjedpetrL91FM29NDSaoixS+2bIJ79Vu7PF+O5r9r8Om5ha89957YW9T3QEAFkg+L6oPHQRgwpat2+A6tBWABcU2gUUffhDNPyUlYn3dba4Nvlfh7sR7772Hsk5gcj8TcszAp9Xa4hCr8EScS7VmFwBY4PH6lNs3Nvk/B1evWoWuXfHLVPkrG/1f4R8t+hgFUS7R6s3cCQHUt/vHv+6rz7G3D/fh4GddbDh/seH8GZOKeevo6IjqdlkRgP3hD3+AxWLB7bffHvb66upqVFRUaC6zWCwoLS1FdXV1t489a9YsFBYWxm2s8eR2u7Fo0SLMnDkTVmt6LZAWQuCO5f4X/gWzZ6A8P/2+ueM9f+V7G/D37asAAOPGjMbcaSNjfsx0lc6vvXTWm3l7u3EttjXXKr+fPukUzDy+opt7+JXvbcCCLauQ68jH3LlTw95m8+EWYP1XcOTaMWJ4BZbXHMSIY47FkLI8YPMmjB5YirlzJ/XuH5cEcXvdbazGyzs3AAAG9SvB3LmnAgCuA/D+pmp8+u8Nmpv3LynA3LlTenzY+nYXHly9FAIS5syZA0mS8Oy+5UB7KyafOglnHVtufMxh3LXiI/gEMG36OehfmNPtbY3MXUunG76vPgEAXHz+ecjpg6XV/KyLDecvNpw/Y1I5by0tLVHdLuMDsNWrV+OJJ57AmjVr4t5lCgCsVmvav+jTcYxd7mCpjiPXnnbjU4vX/PUrDGZKC3Jtaf1vjpd0fO1lgmjmza472C3Mi+59lGPz38YnRMTb+yR/hsdmMcNm8X8N+CDhcIu/NHFomSOt/66xvu7stuB9C3Xv1ZL80ECmKMr3c65qmZTJbIHFbIIQ/u8lq8US9zk1myT4vAImc/SP3Zu5a/Wn9JBnM6Mgr/sAL9vxsy42nL/YcP6MScW8Rft8GdGEozufffYZampqMHToUFgsFlgsFuzbtw933303hg8fDgCorKxETY22rbDH40FDQwMqKytTMOrs51R1Wcux9I2zpuomHH3xTDHFl36z4Dx7dK8pc6A7Q3eNGdyB96fdYlL2G/P4RLADYhY34ACCe6wBQKGuuUZBmG6HhVHsAQaENsZ4e/1hbDniPxsa7yYcQPimH/HUILegZwMOIqK4yvgM2DXXXINzzz1Xc9l5552Ha665Bt///vcBAKeffjqampqwevVqnHLKKQCAJUuWwOfzYfLkyUkfc1/g9PgzYJKkPdjJZsWq09/ebjbBJYqGPgBzRLm1QVT7gAW6INosJqUjoNvrC27CnMUt6AFdF0RdwBWu22E0e4ABUFrZA/75v/21tcrvidgnWW5Fn6h9wBrbGYARESVCRgRgbW1t2Llzp/L7nj17sG7dOpSWlmLo0KEoKyvT3N5qtaKyshLHHXccAGDs2LGYPXs2brjhBjzzzDNwu9249dZbccUVV3TbAZGMc7qDZ9gTURqajuR23oA2A0hkhPr1BES/uXdv9gGzWUxKoPePL/Yq12d7AGZVRUP64CpsABbFHmCAdnNkrz4oSsDHoCmKv3UslD3A8hiAERHFU0aUIK5atQpVVVWoqqoCANx1112oqqrCgw8+GPVjvPLKKxgzZgxmzJiBuXPn4owzzsCzzz6bqCH3eU6lxImleERG2PQZMHscM2ByAGY2aYIR2ZDS3GiHmZG6y4CFC7aiLUFUB2B1rU7NdYksQfQlOABjBoyIKL4yIgM2bdo0iF6UWOzduzfkstLSUrz66qtxHBV1Ry5BtFsyIsaPm1unj8KHm6vxnUlDUj0UynD60t1eZ8C8kbOw4UoQZTlWE/qlYdfSeLJo1oBpg6tw6zf168QiUceyO2vadNfFPwBTgu0ElSByDRgRUWJkRABGmUfJgFn7VgD2k/OOw0/OOy7Vw6AsoF4DZpKiP5khr0PqLgPm1JQgagODgcW5WV82bDWpM2A9fw0WRZkBkyQJFpMEj09gxZ4GzXWJWAMmB3WJasLBNWBERInRt46OKWmCa8BYgkhkhHoNmMNmiTooMpt7sQbMbFKyKLJwXQCzjdUS/DeHC8D0QWm0JYgA0K/Anz18ebl29/lEBLUJ74LY7gbANWBERPHGAIwSoq+WIBLFizoDFm0LeqCXa8DClCDm9oGstbpbYbjgamCxdg1ctE04AODY/gUAgmWeskQkFROeAVNKELM/KCciSqbs/6allJBLnLgfFpEx6iYc0bagB7RdECOtnVXWgJlNIc0+cvvAe7a7fcAAYECRdtPhaNvQA8Bx/fPDXp6QNWDmxLahDzbhyO41gUREycYAjBKiy80MGFEs1CWIRjJgABApMaLNgGkDg9wom31kMnXWL1x2S58B600J3uhABqwo14rLThmsXJ7IfcC66bcSk2AAxgwYEVE88eiYEiLYhp4vMSIjNCWIBjJgAOCJsCF4dyWIfSFrrW69H27N209mHadkxm44c0RIRqw73zppAC6uGoTHLhuvad6RiAxYcB+w+EdgHq8PzZ1cA0ZElAjsgkgJwX3AiGKjLpOLtgU9oF3fFGltkLoEUb8PWF8oQSxx2DCoOBeFuVbkhFnzNrA4F2sfnKUJZqOVZ7Pgz5dPAACs3teoXJ6INWByBiwB8ReaAsGXJAHFDMCIiOKKARglhFMuQewDC/qJEkHfBTFa2gxYhABMlQEz6YKMPpEBM5uw+O6zYTZJEbsTGgm+9NTloInciDkR+4DJLeiLc61xmQsiIgpiAEYJwRJEotjYNCWIxtaAeb09ZMAsJrh1C4j6QgYMSE6gqV5fl5AMmEnOgCUgAOtg+SERUaLw6JgSgiWIRLFRrwFz2KM/V2YyScrBfjQZsJAArA804UgW7WbaiVwDFv8ArMPlAcDXAxFRIjAAo4TgPmBEsbFajGXAgGAWrMcmHGaT8rOsL5QgJotZU4KYgMcPPGYi9gHjViJERInDo2NKCKc7kAHjGjAiQ9SlhBOGFPfqvspeYJFKEFUlwvo1UH2lBDEZ1H/DSGvNYnt8/+drIvYBYxk5EVHi8JOVEkI5e8oSRCJD1KVf046r6NV95QPzHrsgWkyYd9JADC/LU66z8YA7bhJfguj/fyJKELmXIxFR4vCTlRJiT10bAH+7ZyLqvaohxXho3vH43xtP63VQZO5hbZB6DViuzYx3bz9TdV+DA6YQCS9BTGATDpYgEhElDrsgUty1Oz3K/jdTR5WneDREmUmSJHx/6ghD95UzL/r1XbLgGjD/wbX6IFsCW47Hi3ovt0TMq5xVS8gaMGbAiIgShp+sFHdr9zfB7RUYVJyrKW0iouRw2P0BldzJTs+pKkEEtJmaRLRL76vUm2Insg19IvYBYwaMiChxGIBR3O2pbwcAjB1QkJCF50TUPXnj5naXN+z1cgZMnaGh+FPvA6bf8Douj29KXAaMa8CIiBKHn6wUdwcaOgAAQ0sdKR4JUd8kZ8DaneEzYK7ANhHh1paNHVCYuIH1MeoMWCLWgCW0BFHugsgMGBFR3HENGMXdvkAGbGhpbopHQtQ3yRs3RwzAvKEtxhfffTaONndhdP+CxA+wj9BkwBJQDaA04UhECWIgA5bDDBgRUdwxAKO421fvz4ANK2MGjCgVlBLEiBkwbRMOADimXz6O6Zef+MH1IZomHAnIgMkZzEjNVmLR5WYGjIgoUXhqi+LuSHMXAGBQCTNgRKmglCBGWAPmDmzQzD2/EsusbsKRgC6I8qbZHRH+zrFwergGjIgoUfjJSnElhEBb4Kx7Ua41xaMh6pvyAhmwSF0Q1fuAUeJYE7wPmLxZdyICMGbAiIgShyWIFFddbp+yIDzfzpcXUSrkK2vAQg/Mfb7gSRIGYIllMiV2DVheIADrjBBoG+H1CfzPoh34YHM1AGbAiIgSgZ+sFFetTjcA/3oH+eCAiJIrr5suiPe9sVH52WbmV0AimRMegMmZzvhlwJZur8FfPtmp/M59wIiI4o/fvhRXrV3+A758u4V7gBGlSHAfsNAA7N+rDig/MwOWWOqyQykBU62sAXPHLwD7ZHuN5ndmwIiI4o+frBRXbYEArDCH67+IUsXRTQmiGg+uE0vbhCP+giWI8QnAhBBYur1WcxlfI0RE8cdPVooreW0J138RpY7DFixB/OybWlz3wtc42NgBodsviiWIiWWWEluCmKsLwJ7/bDfO/uMnONLcaejxdtW24WCj9r4sQSQiij9++1JcKSWIOQzAiFJFyYC5vLjm71/j0x21uP+/m5QTJDJTIlrzkUIdcyV0DVigBPG3727FvvoO/PmjHYYe75NttSGXMQNGRBR//GSluGIGjCj1HGGacNS2OtHY7lZ+v+HMEUkfV1+jbsKRiCWxkbogurzGNmaW13+pM6P9CuwGR0dERJEwAKO4auvyH+AxA0aUOnIGTL0PmMUkoaHDBQAYVJyLn3/r+JSMrS9JdBfEeO4D1trlxsq9DQCAB+cdjxHlDjx4/vEYXJIX82MTEZEWj5IpruQMWAEzYEQpI3dBVJccmk0SGtqdAIBShy0l4+prTFL4n+Mlnk04vthZD7dXYES5A989bRi+e9qwmB+TiIjCYwYsgzg9Xny4uTpuHa8SQd2GnohSQ86AdbmDpWgWk4SGQAliCQOwpCjJC85zIrblyLPGbx+wpYHyw2nH9Yv5sYiIqHs8Ss4gt7+2Fh9uPorbzhmFu2cdl+rhhFXT6j/DzgM8otQJtwm62SShsd1fgljG92dSlOXb8ew1p8BuNWvKEeNF6YLo9sLnEz3cunsbDjYDAKYcUx7zuIiIqHvMgGWIJduO4sPNRwEA/1y+L8WjiWx3bRsAYGS5I8UjIeq77BYTLLoDfos5uAZMnZmhxJp1QiXOHp2YrJI60O7yxJYFa+70Z0fZdIOIKPEYgGWANqcHv/jvJuX3li43mgIHUulECIHdte0AgJH98lM8GqK+S5KkkCyY2WRCQ5v/c6PUwY3Ss0Gu1awE2k0d7h5u3b2WQABWlMvXBhFRojEAywCPfbgdh5u7MLgkF+X5NggB7Djaluphhahtc6LV6YEkAcPK2DmLKJX06zCtqi6ILBHODiaThMqiHADA4SZjmy8DgNcn0Bpo2FLIDrZERAnHACwDzDqhP0aUO/D7i8YpmaXqlq4UjyrUrhp/9mtISR5yrKFrUIgoefJ0AZh6DVgpSxCzxqDiXADA/oYO5bLerjZr6wp2yyzIYQaMiCjRGIBlgCnHlOOjH5+Fs0b3Q2Wh/2zn0eb0CMD213fg5n+txpr9jdhdF1j/1Y/rv4hSzRFSgiihQQ7AmAHLGoNK/AHYnrp2w48hr//KtZphs/CwgIgo0VhrkCGsZv+Xolxuki4ZsHc2HMb7m6rR5vRgdP8CAMDIcq7/Iko1R5gMmFyCyAAse8gZsFgCsJYufwBWmMtDAiKiZOCnbYbpX5heAZi8cHvF7ga4PP49h5gBI0q9PFvox7uc6eAasOwRLgDz9rIjPRtwEBElF2sNMoxcgnhQVe+fSm2Bhdsurw8r9jQAAI5hB0SilMu3a0sQW7s8EIED82IeaGeNgWECMFcvW9LLgXkh138RESUFA7AMc0yFP7u0/mCzEvCkUrvTE3LZMcyAEaWcvglHbWCT9OI8KyxmfvRnC3kNWIcrGHTJ1QjR8Hh9uPmVNXEfFxERRcZv4QwzprIQ047zb+r5xa76FI8GaHNqz7Tm2y3cyJMoDejb0Ne2+QMwdkDMLnIJoprLG30Api5nH17Ok2dERMnAACwDjaksBAB0unpXZpII+gzYyH4OSFJvmyATUbzpN2KWM2Bc/5VdcqxmlOn+pr3JgDlVt73hzJFxGxcREUXGACwDyQdWne7ov2QTpd3lD8Dkltdc/0WUHvQZMFkJM2BZRy5DlLl60YVDDtb6FdhxXGVBXMdFREThMQDLQLmBTY7TIQMmN+G4ZfoojOznwKWnDE7xiIgICN8FEUBItoQyn74M0UgGzM79v4iIkoZt6DNQrpIBS30AJpcgnj26H340fVSKR0NEMoeuC6KMJYjZZ2BIABb9d4Mz8D3CAIyIKHn4iZuB5BLEjjTIgLUHmnDoN30lotRyRMiAlTrYajzbhGTAetGEQ86A2SzhA3YiIoo/BmAZKE+VAfvooIRfv7stJeMQQgTXgEU4205EqZEXKQPGNWBZJzQDxhJEIqJ0xk/cDJQTWAN2uKkT7x4w4+Wv9qOmtauHe8Vfh8urbOwaacE/EaVGpPdkWT4DsGwzWNeEw92LJhxOD0sQiYiSjZ+4GUheXF/d4lQu680Zz3iR13+ZpGBjECJKD5GacDADln2GlORpfu9VBizQTdfOz3AioqRhAJaB9Pv7AIDXF/0Zz3ipkfcVyrNx7y+iNBMpA1bKJhxZpyjPinvOO075vdPthTvKdWAsQSQiSj5+4mag3DABWG9KTuJlf0MHAGBYWV4PtySiZIu4BowBWFb60fRR2PSr81CYY4HXJ7DlcEtU92MJIhFR8vETNwOFy4B5fMktQfT6BPbWtwMAhpc5kvrcRNSzcF0QrWYJBVyvmbXy7RZMHF4KAFi1rzGq+wQzYCxBJCJKFgZgGSjceitPEjNgf1nyDcb/6iMs2nIUADCUGTCitGM2hZYFs1w4+50yrAQAsHZ/lAGYsgaMhwNERMnCT9wMFK4E0ZPENWCPfbQDbU4P1u5vAsAMGFG6+vJn5+DRS09Sfuf6r+x34qAiAMDWI9GVILq8LEEkIko2fuJmIJvZFHJ229OLjTfjjRkwovQ0sDgXYysLld+L87gJc7YbO6AAALC7rh0dgX0auyNnwGwMwIiIkoafuBlIkiTlTLbV5M98JTMDpscMGFH6Up+sKcplAJbtKgpyUJ5vhxDA9urWHm/PNWBERMnHACxDPXHFBPzp0nEoz/H/nsw1YGoFORaU8Kw6UdqymIMBWEEO36t9gZwF23okmgCMJYhERMnGT9wMNeWYclwwfgDkY6tkd0GUDSvL46J+ojSmzoAV5LADYl9w/EB/2emWI8093pb7gBERJR8/cTOcEoAlKQOmX2s2jOWHRGnNog7A2IK+Tzh+QCAAi2IvsGAXRJYgEhElCwOwDGdKcgas3enV/D6slA04iNKZNgPGEsS+QA7AtlW3wtfD+mCWIBIRJR8/cTNcsAQxORmwNl1XLTbgIEpvFlPwYz6fJYh9wohyB+wWEzpcXuxr6Oj2tixBJCJKPn7iZjiTFOiCmKQSxKYOl+Z3tqAnSm9cA9b3WMwmjKn0N+LoqQyxttUJACjO4x5xRETJwgAsw8kZMHcS9gF7/rPd+NaTn2suG8YAjCitqdeAOWwMwPqKsQN6bsTh9QklQzaynNUMRETJkhEB2LJlyzBv3jwMHDgQkiThzTff1Fz/y1/+EmPGjIHD4UBJSQnOPfdcrFixQnObhoYGXH311SgsLERxcTHmz5+Ptra2JP4rEkMOwLw+ASESmwX77btbNb8PKs5F/4KchD4nEcVG3YY+h40W+gy5E2J3regPN3XC5fHBZjZhYHFusoZGRNTnZUQA1t7ejvHjx2PBggVhrx89ejT+8pe/YOPGjfj8888xfPhwzJo1C7W1tcptrr76amzevBmLFi3CwoULsWzZMtx4443J+ickjHxye9PhZkz87cf4xxd7kvK8JwwsxJKfnA2TiS3oidKZeg1Yno0BWF8RTSfEvfXtAPyl5GZ+lhMRJU1G1KPMmTMHc+bMiXj9VVddpfn9z3/+M/7+979jw4YNmDFjBrZu3YoPPvgAK1euxMSJEwEATz31FObOnYvHHnsMAwcOTOj4E0k+uf2vr/YDAH71zhZ8f+qIhD9v/8Ic2C08mCNKd6r4iwFYHzImEIBVt3Shod2FAltogPXFznoAwDH9WH5IRJRMGRGA9YbL5cKzzz6LoqIijB8/HgCwfPlyFBcXK8EXAJx77rkwmUxYsWIFLrroooiP53a74Xa7Ez5uI9xuN8KdtEzEePXljcNKc9N2XqIljz/T/x2pwLkzJhXzJgkBkwT4BFBZYM3ovxlfd9Gzm4CBRTk43NyFW19ZDZMEXFQenLvmTjde/movAOCi8QM4pxHwNRcbzl9sOH/GpHLeon3OrAnAFi5ciCuuuAIdHR0YMGAAFi1ahPLycgBAdXU1KioqNLe3WCwoLS1FdXV1t4/70UcfIS8vfRtNmKXQKtL33nsv7s/T5QXUL5fO6t14771dcX+eVFi0aFGqh5CxOHfGJHveHp4I+AAsWfRhUp83Ufi6i47XZQYg4cvdDQCAqpzg3H1wQEK704wBeQKdu1fhveRUr2csvuZiw/mLDefPmFTMW0dH91t/yLImAJs+fTrWrVuHuro6PPfcc/jOd76DFStWhARevTVr1iwUFhbGaZTx5Xa78b+7FodcPmfOHEhSfOv59zd0AF8HOyBeNON0nDy0OK7PkWxutxuLFi3CzJkzYbVyg9re4NwZw3mLDeevd57e/SWOdgabTXl8EmbOPBdOn4QH/7QMgAf3nj8e3xpXmbpBpjm+5mLD+YsN58+YVM5bS0v3W3/IsiYAczgcGDVqFEaNGoXTTjsNxx57LP7+97/jvvvuQ2VlJWpqajS393g8aGhoQGVl9188Vqs1rV/04UoQnT4JBTnxHXOLU9vmfsyA4rSel95I979xOuPcGcN5iw3nLzo2XdfLDo9/7j7dVofmTg+GleVh3oTBbMARBb7mYsP5iw3nz5hUzFu0z5cRXRCN8Pl8cDr9G0yefvrpaGpqwurVq5XrlyxZAp/Ph8mTJ6dqiHFhDvO92dDuCr0wRo2qDZjf/NFUFOXxg4CIKJ1ZdIFVu8f//zan/4cR5Q4GX0REKZARGbC2tjbs3LlT+X3Pnj1Yt24dSktLUVZWht/97ne44IILMGDAANTV1WHBggU4dOgQLrvsMgDA2LFjMXv2bNxwww145pln4Ha7ceutt+KKK67I6A6IQPgMWEO7C8PK4tvVqr7NH4CdPbofJgwpjutjExFR/FnN2nOsHYEAzOvzN1XSB2hERJQcGRGArVq1CtOnT1d+v+uuuwAA1113HZ555hls27YNL730Eurq6lBWVoZJkybhs88+wwknnKDc55VXXsGtt96KGTNmwGQy4ZJLLsGTTz6Z9H9LvIXLgKmzVfEiZ9XKHLa4PzYREcVfaADm/8JwKwFY1hbBEBGltYwIwKZNmxbSBl3tjTfe6PExSktL8eqrr8ZzWGkhXAAmZ6viSQ7AShiAERFlBKvuC0LOgHm8/jW9lnBfIERElHA8/ZXhTFJoYJqIDFh9IAArZQBGRJQRLBFKED1e//eGPkNGRETJwU/fDBe+CUf8N55jCSIRUWax6QKs9kAJoidQgsgGHEREqcEALMOFXQOWgC6IzIAREWUWfYmhvgRRX6JIRETJwQAsw6lPYMo/1ycgAGto97f0L8tnAEZElAkidUFkEw4iotTip2+GU5/AHFqaByBBXRDb5AyYPe6PTURE8ReuCYcQQsmAsQSRiCg1GIBlOPUJziFyABbnDFiX24t2lxcASxCJiDKFPgPmFRI6XF5lHzCWIBIRpQYDsAyn/gMOLvEHYPEuQZQbcFjNEgpzMmLnAiKiPi9ciWFLlwfuQBdEfZdEIiJKDn76Zjj1CcyTBhcBAJo73UqJSTwoe4Dl2SBJPGNKRJQJrJbQz+umDjc8vkATDpYgEhGlBAOwDNes6jg/b/xAyPFRU2f8WtGzAyIRUeaxhsmANXe6lQyYmU04iIhSgp++Ge6UcoHiXCtuP2cU8u0WFOVaAQSzVvHADohERJlHvQYs1+r/uanTDW8gA6ZvU09ERMnBBT0ZrtQOfH3fNFit/sCrNM+Gpg53XAOwenZAJCLKOOoAqzzfjgONnYESdTbhICJKJWbAsoAkScraLLlMsLHdhT99tB0z//xpzMGYfP/SPGtsAyUioqSxqTJg/Qr8J9CaOtzcB4yIKMX46ZtlSgIBWH27Cws3HME3NW34ZFsNAOCR97fhuhe+7nWDDnlfMWbAiIgyhzYD5v9uUDdpYgkiEVFqMADLMqV5wQxYl9u/d9e6A00AgGc+3YVPd9Tis5112F7dig6Xp8fHW7m3Aa99fcD/2A5mwIiIMoV6DVhFIAOmbsLBDBgRUWrw0zfLlAbOcjZ0uNAZCMDWHmjU3Gb5rnqc9/gyzH9xVbeP5fMJXPbMcuX3wlwGYEREmcJs0q4BA9iEg4goHTAAyzJyBqxBlQHbdqQV7c5gtuv9TUcAAMt31+PDzdURH2tnbZvm9+I8dkEkIspE/dQliD424SAiSiUGYFlGXgPmD8D8Zzk9PoFV+4JZsNpWp/Lzba+tVQI1vbX7tZmzYmbAiIgyhhDBn8vlEsQON9yBNWDcB4yIKDX46ZtlygIB2JHmLs3lK3bXKz/LgRkAuDw+NEfYtHnz4RbN70UMwIiIMlJ54LuhSd2G3sQMGBFRKjAAyzJyBuxIU6fm8uWqAEyvtSt8Mw59EFfMNvRERBlDIJgCkz+/1SWIFjMPAYiIUoGfvllGXgPW7tKWFa7d3xRy25LAF3KbM3wAVqMqVQSAghwGYEREmUguIe90+5TOuBZmwIiIUoIBWJaRuyDKbBYTwn3HDijKQUVBDgCgLUIGrKZFmwEz88uaiChjqNeA5dstkFQZMYBdEImIUoUBWJZx2MywqcpKCnMsGFySF3K7Y/rlIz/HAgBoc4auAfP5REgGjIiIMoc63DKZJORZtNdzHzAiotTgp2+WkSQJJaoNk3OsZhTmWkJuN7KfA/l2/+X6NWBurw/f1LTB6wt+fQ8syknQiImIKBHyrGbt77qvArahJyJKjdAjc8p4pQ47jrb4s1c5VjMK7KFrt0aWO1Df7gKgXQPW5vTg3D99iupA+WG/Ajueu3YihpWGZtGIiCh9zRs/EO9sOIzTRpYBCA3AWFZORJQaDMCyUJ4teNYzx2qKkAHLx7bqVgDQbNK8p7ZdCb4cNjMunzgEE4YUJ3bAREQUdzaLCS9+/1QAgNvtRp5FAAgGXVZ2QSQiSgkGYFlIvQYs12oO271QU4KoCsC6PP7uiSPKHfjkJ9MSO1AiIkqaXG1FIptwEBGlCE9/ZSGbJfhnzbGaUZCjjbNzrCYMLMoNNuFQrQHrDLSvt1v40iAiyib6eItNOIiIUoOfvllIH4AV6jJgx1UWwmSSlAyYeg1Yl9ur3I+IiLJISADGDBgRUSowAMtCPWXA/nDJOABQLm/uDLah7/L4APhLF4mIKHvowy2WIBIRpQYDsCxkV60By7GYNBmwy04ZjDGVhQCAyqJcAMCRJn/TjQMNHbj9tbX++1n50iAiymaSxACMiCgVeJSdhdQZsFybdh8wuyqwGlziD8AONHZACIFbX12jXMcSRCKi7HJahU/zu5UliEREKcEuiFkotAQxmAGzW4KB1aBifwDW4fKiscON9QebletYgkhElF2OKQQ+umMqVu1vgcfnQ0VhTqqHRETUJzEAy0I2XQni2AGFyu+HmzqD11nN6F/o37T5QEOH5jHsDMCIiLLOiHIHRg8oTvUwiIj6NJYgZiFNBsxmRqnDhp/MGg0AuHDCQM1th5TkAQD+veqA5nKuASMiIiIiij9mwLKQJgALlBzees6xuHbK8JCW9NdNGY7V+xvx6or9msu5BoyIiIiIKP6Y5shC+jVgMn3wBQDzxg/EHy8dD/1abK4BIyIiIiKKPwZgWUi9BizX1vOf+NJTBuPxK6o0l7EEkYiIiIgo/niUnYXsYUoQe3LB+IF480dTld+tZr40iIiIiIjijUfZWShSCWJPjld1S/R4RVzHREREREREDMCykjp71ZsATB24eXwMwIiIiIiI4o0BWBbSZsCM/Yk9Xl+8hkNERERERAEMwLKQzWAGTM3CNWBERERERHHHo+wspM6A9bad/D3nHYcTBxXi6tOGxntYRERERER9HjdizkJGm3AAwI+mj8KPpo+K95CIiIiIiAjMgGUlsxTcVZn7eRERERERpQ8enWchdQNDo2vAiIiIiIgo/hiAZSGfCEZg6k2ZiYiIiIgotXh0noXUWS9JVY5IRERERESpxSYcWejkocW4fOIQDC93pHooRERERESkwgAsC0mShD9celKqh0FERERERDosQSQiIiIiIkoSBmBERERERERJwgCMiIiIiIgoSRiAERERERERJQkDMCIiIiIioiRhAEZERERERJQkDMCIiIiIiIiShAEYERERERFRkjAAIyIiIiIiShIGYEREREREREnCAIyIiIiIiChJGIARERERERElCQMwIiIiIiKiJGEARkRERERElCQMwIiIiIiIiJIkIwKwZcuWYd68eRg4cCAkScKbb76pXOd2u3Hvvfdi3LhxcDgcGDhwIK699locPnxY8xgNDQ24+uqrUVhYiOLiYsyfPx9tbW1J/pcQEREREVFflhEBWHt7O8aPH48FCxaEXNfR0YE1a9bggQcewJo1a/DGG29g+/btuOCCCzS3u/rqq7F582YsWrQICxcuxLJly3DjjTcm659AREREREQES6oHEI05c+Zgzpw5Ya8rKirCokWLNJf95S9/wamnnor9+/dj6NCh2Lp1Kz744AOsXLkSEydOBAA89dRTmDt3Lh577DEMHDgw4f8GIiIiIiKijAjAequ5uRmSJKG4uBgAsHz5chQXFyvBFwCce+65MJlMWLFiBS666KKIj+V2u+F2uxM9ZEPkcaXr+NId5884zp0xnLfYcP6M49wZw3mLDecvNpw/Y1I5b9E+Z9YFYF1dXbj33ntx5ZVXorCwEABQXV2NiooKze0sFgtKS0tRXV3d7eN99NFHyMvLS9h440GfAaTe4fwZx7kzhvMWG86fcZw7YzhvseH8xYbzZ0wq5q2joyOq22VVAOZ2u/Gd73wHQgg8/fTTcXnMWbNmKYFcunG73Vi0aBFmzpwJq9Wa6uFkHM6fcZw7YzhvseH8Gce5M4bzFhvOX2w4f8akct5aWlqiul3WBGBy8LVv3z4sWbJEEzRVVlaipqZGc3uPx4OGhgZUVlZ2+7hWqzXtX/SZMMZ0xvkzjnNnDOctNpw/4zh3xnDeYsP5iw3nz5hUzFu0z5cRXRB7Igdf33zzDT7++GOUlZVprj/99NPR1NSE1atXK5ctWbIEPp8PkydPTvZwiYiIiIioj8qIDFhbWxt27typ/L5nzx6sW7cOpaWlGDBgAC699FKsWbMGCxcuhNfrVdZ1lZaWwmazYezYsZg9ezZuuOEGPPPMM3C73bj11ltxxRVXsAMiERERERElTUYEYKtWrcL06dOV3++66y4AwHXXXYdf/vKXePvttwEAEyZM0Nzvk08+wbRp0wAAr7zyCm699VbMmDEDJpMJl1xyCZ588smkjJ+IiIiIiAjIkABs2rRpEEJEvL6762SlpaV49dVXo35O+TGjXUyXCm63Gx0dHWhpaWFtsAGcP+M4d8Zw3mLD+TOOc2cM5y02nL/YcP6MSeW8yXFDT7FJRgRgqdDa2goAGDJkSIpHQkREREREmaK1tRVFRUURr5dENOmjPsjn8+Hw4cMoKCiAJEmpHg4REREREaUxIQRaW1sxcOBAmEyRex0yACMiIiIiIkqSrGhDT0RERERElAkYgBERERERESUJAzAiIiIiIqIkYQBGRERERESUJAzAiIiIiIiIkoQBGBERERERUZIwAEszBw4cwI4dO1I9DOqjfD5fqoeQcY4ePYrDhw+nehjUh/F923t831Iq8T1rTDa9bxmApZG1a9di4sSJ2LhxY6qHkpF27tyJP/7xj7j33nvx8ssvo66uLtVDyhjNzc0AAJPJxC+GXli7di1OPfVUbNu2LdVDyUh79+7Fc889hyeffBLvv/9+qoeTcfi+NYbv29jwfWsc37PGZdv7lgFYmli/fj3OPPNMXH311bjkkktSPZyMs2nTJpx22mn45JNPsHPnTvzgBz/A5ZdfjnfeeSfVQ0t7W7ZswbBhw/D73/8eAL8YoiW/Zy+66CKcc845qR5Oxtm4cSMmT56M1157Df/9739x/vnn49prr8XXX3+d6qFlBL5vjeH7NjZ83xrH96xxWfm+FZRyW7duFXl5eeL+++8XQgjhdrvFp59+Kt58803xxRdfpHh06a+xsVFMmTJFmT8hhNi4caMwm83ilFNOEf/85z9TOLr0duDAAVFVVSVGjx4tSktLxcMPP6xc5/V6Uziy9LZp0yZRUFAgfvaznwkhhPB4PGLt2rXiiy++EJs2bUrx6NJfXV2dGD9+vPj5z3+uXPbee+8Jk8kk5s2bJ5YsWZLC0aU/vm+N4fs2NnzfGsf3rHHZ+r5lAJZiTqdTXHjhhaKiokJ8/fXXQggh5s2bJ8aPHy8qKiqE1WoVt99+u6itrU3xSNNXTU2NqKqqEkuXLhVer1e0t7cLt9stzjzzTDFhwgQxc+ZMsXnz5lQPM+14vV7x+OOPi4svvlgsWbJEPPLII6KwsJBfDD3o6uoSVVVVYsCAAeLIkSNCCCG+/e1vi6qqKlFaWiocDod49NFHUzzK9LZz505xyimniM2bNwufzyecTqc4fPiwOOGEE0RlZaW4+OKLRUNDQ6qHmZb4vjWG79vY8X1rDN+zxmXz+5YBWBpYuXKlmDVrlpg9e7YYM2aMmD17tli9erXYu3evePvtt4XVahW/+MUvUj3MtLVr1y6Rk5Mj/vOf/yiX7d27V0yePFm88sorori4WPz6179O4QjT144dO8Srr74qhBCioaFBPPzww/xiiMInn3wijjvuOHHFFVeIk08+WcyaNUt89tlnYuXKleLJJ58UkiSJp59+OtXDTFtr164VkiSJxYsXK5ft3LlTzJ49W7zyyitCkiTx7LPPpnCE6Y3vW2P4vo0N37fG8T1rXLa+bxmApYmVK1eKKVOmiJkzZ4o9e/ZornviiSdEv379xKFDh4TP50vNANPcj3/8Y2G328VDDz0knnzySVFUVCRuuukmIYQQf/zjH8XUqVNFe3s75y8M9ZzU1taGnJ3zeDzi7bffZhZWaOfqk08+EZWVleLss88Whw8f1tzu7rvvFuPGjRP19fV8zYXhdrvFNddcI0aNGiX+8pe/iNdee02UlJSIW265RQghxJ133imuuOIK4Xa7OX8R8H0bPb5v44Pv29jwPds72f6+taR6DVpfdODAAWzduhW1tbU499xzUVRUhIkTJ+Jvf/sbtm/fjsGDBwMAhBCQJAmSJGHAgAEoKyuDJEkpHn3qqedv5syZKC0txa9//WsUFhbin//8J/r374+77roLDz74IACgoaEBAJCXl5fKYaeFw4cP49ChQ6ivr8e5554Lk8kEk8kEj8cDi8WC8vJyXH/99QCA3//+9xBCoL6+Hk888QT279+f4tGnjnreZsyYAQCYNm0aFi5ciC1btqBfv36a2+fk5CAvLw8lJSV8z0I7fzNnzoTFYsG9996LBQsW4KGHHkJlZSVuueUW/Pa3vwXg7xTW2NgIi4VfUQDft0bxfRsbvm+N43vWuD7zvk1t/Nf3rF+/XvTv31+cfPLJwmaziRNOOEHcfffdor6+XgghhMvlCrnPHXfcIS699FLR3t6e7OGmHf38jR07Vtx7772isbFRCOE/qyT/LLvxxhvFD37wA+FyuTLq7Ei8rV+/XgwZMkQcf/zxwmKxiKqqKvH000+L1tZWIYT/7JustrZWPPzww0KSJFFSUiJWrlyZqmGnXLh5W7BggWhubhZChH/P/vCHPxTXX3+9cDqdffo1J0To/E2YMEE8++yzoqOjQwghxMGDBzVnNH0+n7j22mvFvffeK3w+H+eP71tD+L6NDd+3xvE9a1xfet8yAEuipqYmcfLJJysBV2dnp7jvvvvElClTxIUXXqgEYbLdu3eLBx54QBQXF2d0p5d4iTR/p59+urjgggtEXV2dECKYtv7mm2/ET3/6U1FYWNjn56+2tlYJVvfs2SNqamrElVdeKSZPnizuvPNO0dLSIoTQ1qBfc801orCwsE83MIl23mSHDx8WDzzwgCgpKenT8yaLNH+TJk0Sd955p2hqatLcfteuXeL+++8XxcXFYsuWLSkadfrg+9YYvm9jw/etcXzPGtfX3rcMwJJoz549YuTIkWLp0qXKZU6nU7zwwgvi9NNPF1dffbXyAtu4caO44IILxPDhw8XatWtTNOL00t38nXbaaeKqq65S5q++vl784he/EBMnThRr1qxJ1ZDTxsaNG8Xw4cPF+vXrlcucTqd48MEHxamnnip+/vOfi87OTiGEP4B9+eWXRf/+/cXq1atTNeS00Jt5+/rrr8Vll10mBg8ezPdsQG/mr7a2Vvzwhz8Uxx13HN+zAXzfGsP3bWz4vjWO71nj+tr7lhsxJ1F+fj7y8vKwceNGAP41XjabDddddx2++93vYuvWrXjzzTcBAMcccwzuuOMOLFmyBBMmTEjdoNNId/N3zTXXYNu2bcr8lZaW4uabb8a7776LqqqqFI46PdhsNkiSpNSWezwe2Gw2PPDAAzj77LPx7rvvYuXKlQAASZIwdepUrFixAieffHIqh51yvZm3AQMG4Dvf+Q6WLl3K92xAb+avvLwc99xzDxYvXsz3bADft8bwfRsbvm+N43vWuL72vpWEECLVg+gr3G43rrzyShw5cgSvvvoqhg0bprn+vPPOg8ViwbvvvpuiEaa3aObParVi4cKFKRph+nI6nTjjjDNQWVmJN998E2azWVkMLITA+PHjUVVVhZdeeklp/kLRzduECRPwz3/+M9VDTUu9ed1RKL5vjeH7NjZ83xrH96xxfe19ywxYkgghYLVa8de//hW7du3C7bffjpqaGqjj33nz5qG+vh5dXV0pHGl6inb+6urqOH86Pp8Pdrsd//jHP7Bs2TLcfPPNAKB8qEmShAsuuAA1NTUAwC+EgGjnrba2NsUjTU+9fd2RFt+3xvB9Gxu+b43je9a4vvi+ZQCWJJIkweVyoaKiAh988AFWrFiB7373u1i1ahW8Xi8AYN26dSgrK4PJxD+LHufPOJPJBK/XixNPPBEvvfQSXnvtNVx77bU4evSocps9e/agpKREmUvivMWK8xcbzp8xnLfYcP6M49wZ1xfnjiWISeL1emE2m1FfXw+Xy4XOzk7MmTMH+fn58Hg8GDlyJBYvXozPP/8cJ510UqqHm3Y4f8bJKfy2tjY4nU6sW7cOV111FYYNG4bS0lKUlZXhrbfewvLlyzFu3LhUDzdtcN5iw/nrHX05EucvOpy32HD+jPP5fJoTvpy76HHumAFLCjl42Lt3L0466SQsXrwYI0eOxMqVK3HnnXdi5syZmDRpElauXMngIQzOn3Hyh9revXsxevRorFy5EjNmzMDmzZsxd+5cDBo0CBUVFfj666+z5kMtHjhvseH8RU8+myufCxVCcP6iwHmLDefPuLq6OgDBrA3gn0/OXc84dyqJbLHY11RXV4vt27eHve7AgQOiuLhY3HDDDcLn82n2gCA/zp9xu3fvFk8//bT48Y9/LD766CNRW1urXLd//35RXl4u5s+fL3w+n7IJpLxfWl+eS85bbDh/sdm+fbu48847xcUXXyx+9atfid27dyvXcf4i47zFhvNn3Pbt20VBQYG44YYblMvkOeLcdY9zp8UMWJxs3boVp556Kh544AFs3rw55PpVq1Zh/vz5+Nvf/gZJkrhOSYfzZ9zGjRtxxhln4O2338bChQtx22234YUXXoDX64Xb7cbbb7+Na665Bs899xwkSYLZbNbcv68uBOa8xYbzF5uNGzdiypQpaGxshM/nw/vvv4/XXnsNQgi43W689dZb+O53v8v50+G8xYbzF5stW7YgNzcXGzduxE033QQAMJvNcLlcymeefJzCudPi3OmkOADMCocOHRJTpkwR48ePF6eeeqqYP3++2Lhxo+Y2LpcrRaNLf5w/4/bu3SuOPfZYcf/99ytz9LOf/UyMGjVK2bCwqakplUNMS5y32HD+YrNr1y4xbNgw8fOf/1y5bP78+eL222/X3E4+C0x+nLfYcP5i995774nRo0eLRx55RIwbN07cdNNNynUHDhxI4cjSH+dOi2mEONi2bRsKCgrw0ksv4ZZbbsHatWvx+OOPY9OmTcptrFZrCkeY3jh/xni9Xrz11luoqqrCbbfdpmQF77zzTrhcLuzYsQMAUFRUlMphph3OW2w4f7Hxer1YtGgRZsyYgbvvvltZg5Obm4tNmzbh7LPPxrXXXosvv/wSZrNZs9VGX8Z5iw3nLz7GjRuHU045BT/4wQ/w/e9/H8uXL8ddd92F+fPn491334Xb7U71ENMW507LkuoBZIMpU6bgoYcewvjx4zF+/Hj4fD785S9/weOPP4477rhDWTQoAt2G9N1f+jrOnzFmsxlFRUWYOnUqKisrlcslSUJLSwsaGhpC7iMEN37kvMWG8xcbs9mMWbNm4YwzzkBJSQkA4Ne//jWef/55PPjgg+jq6sL27dtxzTXX4OOPP8aIESNSPOL0wHmLDecvPkpLS7F582YcOHAAN910E/Lz83HfffehoaEBd955J6xWq9I4jLQ4dzopyrxlHXmhoOzFF18UJ598sqac7le/+pVYv359KoaX9jh/sZPnsLOzU4wZM0asWLFCue6tt94S+/fvT9XQ0hrnLTacP2Pkeevq6hJz584VCxcuVK777LPPREVFhfjoo49SNby0xXmLDefPOJfLJTwej5g1a5b47LPPhBBCXH755aKwsFAce+yxIaWcFMS5C8UMmAGHDx/GmjVr4HK5MGzYMJxyyimQJAlCCAghYDKZcN111wEAnnzySTzxxBNoaWnB//t//w+XXnppikefepw/49RzN3ToUEycOBEANGeNTCYTTCaTknG4//778Y9//AMrVqxI2bhTjfMWG85fbCJ95nm9XtjtdrzzzjswmUxKdr+0tBT9+/dHaWlpqoeeUpy32HD+jFPP3fDhw3HyyScrSyFOOeUU7Ny5E88++yyWLVuGd955Bxs3bsQjjzwCi8WCP/3pTykefWpx7qKU2vgv82zYsEGMHDlSnHrqqaK8vFxMnDhR/N///Z/mNup2mX//+9+F1WoVRUVFYu3atUkebfrh/BkXzdwJIURjY6Po16+f+OKLL8RvfvMbkZOTI1auXJmCEacHzltsOH+xiWb+9BUAP/vZz8SkSZM0bf37Gs5bbDh/xvU0d7/85S+FJElixIgRYvXq1UII/+ffX//6V7Fr165UDTstcO6ixwCsF3bu3CkGDx4sfvrTn4qmpiaxatUqcd1114nrr79eeDwezYeZvI/B7bffLkpKSsSmTZtSOPL0wPkzrjdz19raKqqqqsS0adNETk6OWLVqVQpHnlqct9hw/mLTm/kTQoh9+/aJe+65R5SUlPTpcmvOW2w4f8Z1N3dut1sIIYTb7Ra33HKL+Prrr4UQ2b1XVW9w7nqHAViUnE6nuOuuu8R3vvMd4XQ6lcv//ve/i7KyMlFXVxdyn6+//lpIksSzwILzF4vezl1TU5MYNmyYKC0tFevWrUv2cNMG5y02nL/Y9Hb+Vq5cKW655RYxfvz4Pj1/nLfYcP6MM3KcQn6cu97jGrAo+Xw+DB48GGPHjoXNZlO6ek2ZMgX5+flh22dOmjQJDQ0NKC4uTv6A0wznz7jezl1RURFuuOEGXHLJJRgzZkyKRp16nLfYcP5i09v5mzhxIjo7O/GLX/wCAwYMSNGoU4/zFhvOn3FGjlPYldmPc2dAqiK/TLR7927lZzlteuTIETFq1ChNp681a9aE3I44f7GIdu76erZQj/MWG85fbKKdP5ZranHeYsP5M87IcQr5ce56pw+Hnj07cuQIvv76a3zwwQfw+XzKvhher1fp9NXc3IzGxkblPg8++CBmzJiB+vr6Pr/3DefPOKNzN2vWLGXu+iLOW2w4f7ExOn8zZ87s0/PHeYsN58+4eByn9FWcuxilKvJLd+vXrxfDhg0To0ePFkVFRWLMmDHi1VdfFfX19UKIYHS/fft20a9fP9HQ0CB+85vfiNzcXJ5VEpy/WHDujOG8xYbzFxvOnzGct9hw/ozj3BnHuYsdA7AwampqxJgxY8T9998vdu3aJQ4dOiQuv/xyMXbsWPHQQw+Jmpoa5bZHjx4VVVVV4vLLLxc2m40vLMH5iwXnzhjOW2w4f7Hh/BnDeYsN5884zp1xnLv4YAAWxubNm8Xw4cNDXij33nuvGDdunHj00UdFe3u7EEKILVu2CEmSRG5ubp/fp0rG+TOOc2cM5y02nL/YcP6M4bzFhvNnHOfOOM5dfHANWBhutxsejwcdHR0AgM7OTgDAI488gunTp+Ppp5/Gzp07AQAlJSW45ZZbsGbNGkyYMCFVQ04rnD/jOHfGcN5iw/mLDefPGM5bbDh/xnHujOPcxYckRF9fBRfeqaeeivz8fCxZsgQA4HQ6YbfbAfjbo48aNQqvvfYaAKCrqws5OTkpG2s64vwZx7kzhvMWG85fbDh/xnDeYsP5M45zZxznLnbMgAFob29Ha2srWlpalMv+9re/YfPmzbjqqqsAAHa7HR6PBwBw1llnob29XbltX39hcf6M49wZw3mLDecvNpw/YzhvseH8Gce5M45zlxh9PgDbsmULLr74Ypx99tkYO3YsXnnlFQDA2LFj8cQTT2DRokW47LLL4Ha7lQ3jampq4HA44PF4+nwbTc6fcZw7YzhvseH8xYbzZwznLTacP+M4d8Zx7hLHkuoBpNKWLVtw1lln4dprr8XEiROxevVqfP/738fxxx+PqqoqXHDBBXA4HLjllltw0kknYcyYMbDZbHj33Xfx1VdfwWLp09PH+YsB584YzltsOH+x4fwZw3mLDefPOM6dcZy7xOqza8AaGhpw5ZVXYsyYMXjiiSeUy6dPn45x48bhySefVC5rbW3Fb3/7WzQ0NCAnJwc333wzjj/++FQMO21w/ozj3BnDeYsN5y82nD9jOG+x4fwZx7kzjnOXeH02PHW73WhqasKll14KAPD5fDCZTBgxYgQaGhoAAMLfph8FBQX4wx/+oLldX8f5M45zZwznLTacv9hw/ozhvMWG82cc5844zl3i9dlZ6t+/P/71r3/hzDPPBAB4vV4AwKBBg5QXjyRJMJlMmoWHkiQlf7BpiPNnHOfOGM5bbDh/seH8GcN5iw3nzzjOnXGcu8TrswEYABx77LEA/BG71WoF4I/oa2pqlNs8/PDDeP7555XuLnxxBXH+jOPcGcN5iw3nLzacP2M4b7Hh/BnHuTOOc5dYfbYEUc1kMkEIobxw5Oj+wQcfxG9/+1usXbuWiwm7wfkzjnNnDOctNpy/2HD+jOG8xYbzZxznzjjOXWL06QyYmtyLxGKxYMiQIXjsscfw6KOPYtWqVRg/fnyKR5f+OH/Gce6M4bzFhvMXG86fMZy32HD+jOPcGce5iz+GrAFyRG+1WvHcc8+hsLAQn3/+OU4++eQUjywzcP6M49wZw3mLDecvNpw/YzhvseH8Gce5M45zF3/MgOmcd955AIAvv/wSEydOTPFoMg/nzzjOnTGct9hw/mLD+TOG8xYbzp9xnDvjOHfx02f3AetOe3s7HA5HqoeRsTh/xnHujOG8xYbzFxvOnzGct9hw/ozj3BnHuYsPBmBERERERERJwhJEIiIiIiKiJGEARkRERERElCQMwIiIiIiIiJKEARgREREREVGSMAAjIiIiIiJKEgZgREREREREScIAjIiIiIiIKEkYgBERERERESUJAzAiIiIiIqIk+f/DEOugjkXM8QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.style.use('_mpl-gallery')\n",
    "plt.title('Close Price History')\n",
    "plt.plot(data['Close'],label='AAPL')\n",
    "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
    "fig.autofmt_xdate(rotation= 45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1942f4ff-abf4-40b3-8b79-14ad7c183f26",
   "metadata": {},
   "source": [
    "**特征工程**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2366b00a-b638-40d3-afe4-8988e61918cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Close        1.000000\n",
      "Adj Close    0.999503\n",
      "Low          0.997028\n",
      "High         0.996830\n",
      "Open         0.992867\n",
      "Volume      -0.335998\n",
      "Name: Close, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlation = data.corr()\n",
    "print(correlation['Close'].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a0c068a-30f8-4611-a52b-fddd96b3f90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))  # 设置图片大小\n",
    "sns.heatmap(correlation, annot=True, cmap=sns.cubehelix_palette(dark=.20, light=.95, as_cmap=True), linewidths=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bad8e7-3c57-42ad-8687-49c9988a2095",
   "metadata": {},
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2343df6a-883d-497e-a6b7-a0930fc3cb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "604e4a89-af3d-4930-97bc-370ae347b544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用选定的特征来训练模型\n",
    "features = data.drop(['Adj Close', 'Volume'], axis=1)\n",
    "target = data['Adj Close'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b32433-7556-4418-b7a9-56f3b15dfec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(755, 4) (755, 1)\n"
     ]
    }
   ],
   "source": [
    "# 创建 MinMaxScaler实例，对特征进行拟合和变换，生成NumPy数组\n",
    "scaler = MinMaxScaler()\n",
    "features_scaled = scaler.fit_transform(features)\n",
    "target_scaled = scaler.fit_transform(target)\n",
    "print(features_scaled.shape, target_scaled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6641cba3-2a9d-4413-a839-6a10e714359d",
   "metadata": {},
   "source": [
    "**构建时间序列数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a53a60e9-a92e-4dc6-9dc4-c09d9251761f",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = 30\n",
    "X_list = []\n",
    "y_list = []\n",
    "\n",
    "for i in range(len(features_scaled) - time_steps):\n",
    "    X_list.append(features_scaled[i:i+time_steps])\n",
    "    y_list.append(target_scaled[i+time_steps])\n",
    "\n",
    "X = np.array(X_list) # [samples, time_steps, num_features]\n",
    "y = np.array(y_list) # [target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00aa96d0-c5e7-4281-b3a8-969402e3711b",
   "metadata": {},
   "source": [
    "上述代码的目的是进行时间序列数据的预处理，将原始的时间序列数据转换为适合机器学习模型输入的格式。具体来说，它通过滑动窗口的方式将时间序列数据分割成多个样本，每个样本包含一定数量的时间步 `time_steps` 的特征数据以及对应的一个目标值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be463ccd-bf3b-436d-9b30-98f5b3d724c7",
   "metadata": {},
   "source": [
    "`time_steps`：表示每个样本中包含的时间步数。它决定了模型在预测时考虑的历史数据长度。\n",
    "`X_list`：用于存储分割后的特征数据样本的列表。\n",
    "`y_list`：用于存储每个特征数据样本对应的目标值的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4e18d38-ab12-4511-a16f-5216593e4f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples, time_steps, num_features = X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a1aae5-0177-4b6a-9eaf-4e2a41710dd7",
   "metadata": {},
   "source": [
    "**Data set segmentation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd216f89-d2fa-4fc7-8bf8-b0404ae25fda",
   "metadata": {},
   "source": [
    "`train_test_split` split arrays or matrices into random train and test subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a46f0c3-e2ed-4c2d-bd97-1cbf062f2a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(580, 30, 4) (145, 30, 4) (580, 1) (145, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid,\\\n",
    "    y_train, y_valid = train_test_split(X, y, \n",
    "                                        test_size=0.2, \n",
    "                                        random_state=45,\n",
    "                                        shuffle=False)\n",
    "print(X_train.shape, X_valid.shape, y_train.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c6a8ea-130d-4477-9773-e18dd715552e",
   "metadata": {},
   "source": [
    "**Data set tensor (math.)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30dc4ba3-7dc1-4ff4-a5df-6acca350a6d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([580, 30, 4]) torch.Size([145, 30, 4]) torch.Size([580, 1]) torch.Size([145, 1])\n"
     ]
    }
   ],
   "source": [
    "# 将 NumPy数组转换为 tensor张量\n",
    "X_train_tensor = torch.from_numpy(X_train).type(torch.Tensor)\n",
    "X_valid_tensor = torch.from_numpy(X_valid).type(torch.Tensor)\n",
    "y_train_tensor = torch.from_numpy(y_train).type(torch.Tensor).view(-1, 1)\n",
    "y_valid_tensor = torch.from_numpy(y_valid).type(torch.Tensor).view(-1, 1)\n",
    "\n",
    "print(X_train_tensor.shape, X_valid_tensor.shape, y_train_tensor.shape, y_valid_tensor.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f66a4be-85f9-411e-ad8d-b899d21c07af",
   "metadata": {},
   "source": [
    "`.type(torch.Tensor)` 确保张量的数据类型为标准的 `torch.Tensor` 类型，`.view(-1, 1)` 对张量进行维度调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0afc6239-355c-4f34-b3ed-41d53d3ea3b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataHandler(Dataset):\n",
    "    def __init__(self, X_train_tensor, y_train_tensor, X_valid_tensor, y_valid_tensor):\n",
    "        self.X_train_tensor = X_train_tensor\n",
    "        self.y_train_tensor = y_train_tensor\n",
    "        self.X_valid_tensor = X_valid_tensor\n",
    "        self.y_valid_tensor = y_valid_tensor\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.X_train_tensor)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        sample = self.X_train_tensor[idx]\n",
    "        labels = self.y_train_tensor[idx]\n",
    "        return sample, labels\n",
    "        \n",
    "    def train_loader(self):\n",
    "        train_dataset = TensorDataset(self.X_train_tensor, self.y_train_tensor)\n",
    "        return DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "    def valid_loader(self):\n",
    "        valid_dataset = TensorDataset(self.X_valid_tensor, self.y_valid_tensor)\n",
    "        return DataLoader(valid_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59df3ed1-4eae-4e7e-a000-1247cf16f1cc",
   "metadata": {},
   "source": [
    "在上述代码中，定义了一个名为 `DataHandler` 的类，它继承自 `torch.utils.data.Dataset`  \n",
    "`__init__` 方法用于接收数据和标签。 \n",
    "`__len__` 方法返回数据集的长度。 \n",
    "`__getitem__` 方法根据给定的索引 `idx` 返回相应的数据样本和标签。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1990b8ab-cd9f-4259-8478-7a9b879c2904",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_handler = DataHandler(X_train_tensor, y_train_tensor, X_valid_tensor, y_valid_tensor)\n",
    "train_loader = data_handler.train_loader()\n",
    "valid_loader = data_handler.valid_loader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84a68fec-bee5-4730-9b01-d3d0ee96ad34",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM_Transformer(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, lstm_layers, transformer_heads, transformer_layers, output_dim, dropout=0.5):\n",
    "        super(LSTM_Transformer, self).__init__()\n",
    "        # LSTM 层\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, lstm_layers, batch_first=True)\n",
    "        # Transformer 编码器层\n",
    "        transformer_encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=transformer_heads, dim_feedforward=hidden_dim * 2, dropout=dropout, batch_first=True)\n",
    "        self.transformer_encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers=transformer_layers)\n",
    "        # 输出层\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # LSTM 输出\n",
    "        lstm_out, _ = self.lstm(x)\n",
    "        # Transformer 输入\n",
    "        transformer_input = lstm_out\n",
    "        # Transformer 输出\n",
    "        transformer_out = self.transformer_encoder(transformer_input)\n",
    "        # 预测输出\n",
    "        output = self.fc(transformer_out[:, -1, :])\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaec6401-1758-4724-93e4-f8035d3250c2",
   "metadata": {},
   "source": [
    "**实例化模型、损失函数和优化器**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91df0fac-fc7e-4dc0-b998-76a548204e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_features  # 输入特征维度\n",
    "hidden_dim = 64  # LSTM 和 Transformer 的隐藏维度\n",
    "lstm_layers = 1  # LSTM 层数\n",
    "transformer_heads = 8  # Transformer 头数\n",
    "transformer_layers = 1  # Transformer 层数\n",
    "output_dim = 1  # 输出维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44770166-d202-4977-922c-62acf3fecc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTM_Transformer(input_dim, hidden_dim, lstm_layers, transformer_heads, transformer_layers, output_dim)\n",
    "criterion_mse = nn.MSELoss()  # 定义均方误差损失函数\n",
    "criterion_mae = nn.L1Loss()  # 定义平均绝对误差损失\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001) # 定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76ae0a9e-d0be-4b14-a7ec-0780a8df9207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===============================================================================================\n",
       "Layer (type:depth-idx)                        Output Shape              Param #\n",
       "===============================================================================================\n",
       "LSTM_Transformer                              [32, 1]                   --\n",
       "├─LSTM: 1-1                                   [32, 30, 64]              17,920\n",
       "├─TransformerEncoder: 1-2                     [32, 30, 64]              --\n",
       "│    └─ModuleList: 2-1                        --                        --\n",
       "│    │    └─TransformerEncoderLayer: 3-1      [32, 30, 64]              33,472\n",
       "├─Linear: 1-3                                 [32, 1]                   65\n",
       "===============================================================================================\n",
       "Total params: 51,457\n",
       "Trainable params: 51,457\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 17.21\n",
       "===============================================================================================\n",
       "Input size (MB): 0.02\n",
       "Forward/backward pass size (MB): 0.49\n",
       "Params size (MB): 0.07\n",
       "Estimated Total Size (MB): 0.58\n",
       "==============================================================================================="
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# batch_size, seq_len(time_steps), input_size(in_channels)\n",
    "summary(model, (32, time_steps, num_features)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9858c070-63c8-4484-bea5-375c590e5f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer):\n",
    "    epoch_loss_mse = 0\n",
    "    epoch_loss_mae = 0\n",
    "\n",
    "    model.train()  # 确保模型处于训练模式\n",
    "    for batch in iterator:\n",
    "        optimizer.zero_grad()  # 清空梯度\n",
    "        inputs, targets = batch  # 获取输入和目标值\n",
    "        outputs = model(inputs)  # 前向传播\n",
    "\n",
    "        loss_mse = criterion_mse(outputs, targets)  # 计算损失\n",
    "        loss_mae = criterion_mae(outputs, targets)\n",
    "\n",
    "        combined_loss = loss_mse + loss_mae  # 可以根据需要调整两者的权重\n",
    "\n",
    "        combined_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss_mse += loss_mse.item()  # 累计损失\n",
    "        epoch_loss_mae += loss_mae.item()\n",
    "\n",
    "    average_loss_mse = epoch_loss_mse / len(iterator)  # 计算平均损失\n",
    "    average_loss_mae = epoch_loss_mae / len(iterator)\n",
    "\n",
    "    return average_loss_mse, average_loss_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8eb6c86-fa82-4464-be0a-a744704fe689",
   "metadata": {},
   "source": [
    "上述代码定义了一个名为 `train` 的函数，用于训练给定的模型。它接收模型、数据迭代器、优化器作为参数，并返回训练过程中的平均损失。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5d89a8c-5322-459a-803c-16d53714398d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator):\n",
    "    epoch_loss_mse = 0\n",
    "    epoch_loss_mae = 0\n",
    "\n",
    "    model.eval()  # 将模型设置为评估模式，例如关闭 Dropout 等\n",
    "    with torch.no_grad():  # 不需要计算梯度\n",
    "        for batch in iterator:\n",
    "            inputs, targets = batch\n",
    "            outputs = model(inputs)  # 前向传播\n",
    "\n",
    "            loss_mse = criterion_mse(outputs, targets)  # 计算损失\n",
    "            loss_mae = criterion_mae(outputs, targets)\n",
    "\n",
    "            epoch_loss_mse += loss_mse.item()  # 累计损失\n",
    "            epoch_loss_mae += loss_mae.item()\n",
    "\n",
    "    return epoch_loss_mse / len(iterator), epoch_loss_mae / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c22781c-cecd-45dc-8f91-3d0a60f12027",
   "metadata": {},
   "source": [
    "上述代码定义了一个名为 `evaluate` 的函数，用于评估给定模型在给定数据迭代器上的性能。它接收模型、数据迭代器作为参数，并返回评估过程中的平均损失。这个函数通常在模型训练的过程中定期被调用，以监控模型在验证集或测试集上的性能。通过评估模型的性能，可以了解模型的泛化能力和训练的进展情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6400c3d-9e4f-48ce-a6ca-1963760a4435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01, Train MSELoss: 0.29266, Train MAELoss: 0.430, Val. MSELoss: 0.05631, Val. MAELoss: 0.186\n",
      "Epoch: 02, Train MSELoss: 0.10958, Train MAELoss: 0.262, Val. MSELoss: 0.04612, Val. MAELoss: 0.188\n",
      "Epoch: 03, Train MSELoss: 0.07749, Train MAELoss: 0.220, Val. MSELoss: 0.00840, Val. MAELoss: 0.076\n",
      "Epoch: 04, Train MSELoss: 0.06065, Train MAELoss: 0.195, Val. MSELoss: 0.00563, Val. MAELoss: 0.065\n",
      "Epoch: 05, Train MSELoss: 0.05942, Train MAELoss: 0.196, Val. MSELoss: 0.00692, Val. MAELoss: 0.072\n",
      "Epoch: 06, Train MSELoss: 0.04303, Train MAELoss: 0.163, Val. MSELoss: 0.00770, Val. MAELoss: 0.074\n",
      "Epoch: 07, Train MSELoss: 0.04527, Train MAELoss: 0.172, Val. MSELoss: 0.00927, Val. MAELoss: 0.081\n",
      "Epoch: 08, Train MSELoss: 0.03744, Train MAELoss: 0.154, Val. MSELoss: 0.00470, Val. MAELoss: 0.060\n",
      "Epoch: 09, Train MSELoss: 0.03226, Train MAELoss: 0.143, Val. MSELoss: 0.00430, Val. MAELoss: 0.054\n",
      "Epoch: 10, Train MSELoss: 0.03253, Train MAELoss: 0.142, Val. MSELoss: 0.00384, Val. MAELoss: 0.054\n",
      "Epoch: 11, Train MSELoss: 0.02774, Train MAELoss: 0.132, Val. MSELoss: 0.00389, Val. MAELoss: 0.054\n",
      "Epoch: 12, Train MSELoss: 0.02637, Train MAELoss: 0.132, Val. MSELoss: 0.00384, Val. MAELoss: 0.051\n",
      "Epoch: 13, Train MSELoss: 0.02100, Train MAELoss: 0.116, Val. MSELoss: 0.00354, Val. MAELoss: 0.052\n",
      "Epoch: 14, Train MSELoss: 0.02107, Train MAELoss: 0.118, Val. MSELoss: 0.00537, Val. MAELoss: 0.061\n",
      "Epoch: 15, Train MSELoss: 0.01794, Train MAELoss: 0.107, Val. MSELoss: 0.00318, Val. MAELoss: 0.049\n",
      "Epoch: 16, Train MSELoss: 0.01809, Train MAELoss: 0.107, Val. MSELoss: 0.00422, Val. MAELoss: 0.054\n",
      "Epoch: 17, Train MSELoss: 0.01811, Train MAELoss: 0.106, Val. MSELoss: 0.00336, Val. MAELoss: 0.050\n",
      "Epoch: 18, Train MSELoss: 0.01582, Train MAELoss: 0.097, Val. MSELoss: 0.00291, Val. MAELoss: 0.046\n",
      "Epoch: 19, Train MSELoss: 0.01393, Train MAELoss: 0.095, Val. MSELoss: 0.00295, Val. MAELoss: 0.046\n",
      "Epoch: 20, Train MSELoss: 0.01394, Train MAELoss: 0.092, Val. MSELoss: 0.00320, Val. MAELoss: 0.045\n",
      "Epoch: 21, Train MSELoss: 0.01212, Train MAELoss: 0.086, Val. MSELoss: 0.00256, Val. MAELoss: 0.041\n",
      "Epoch: 22, Train MSELoss: 0.01236, Train MAELoss: 0.089, Val. MSELoss: 0.00250, Val. MAELoss: 0.041\n",
      "Epoch: 23, Train MSELoss: 0.00999, Train MAELoss: 0.080, Val. MSELoss: 0.00240, Val. MAELoss: 0.040\n",
      "Epoch: 24, Train MSELoss: 0.00927, Train MAELoss: 0.075, Val. MSELoss: 0.00230, Val. MAELoss: 0.039\n",
      "Epoch: 25, Train MSELoss: 0.00921, Train MAELoss: 0.078, Val. MSELoss: 0.00226, Val. MAELoss: 0.039\n",
      "Epoch: 26, Train MSELoss: 0.00749, Train MAELoss: 0.069, Val. MSELoss: 0.00231, Val. MAELoss: 0.040\n",
      "Epoch: 27, Train MSELoss: 0.00765, Train MAELoss: 0.069, Val. MSELoss: 0.00224, Val. MAELoss: 0.039\n",
      "Epoch: 28, Train MSELoss: 0.00752, Train MAELoss: 0.066, Val. MSELoss: 0.00230, Val. MAELoss: 0.039\n",
      "Epoch: 29, Train MSELoss: 0.00715, Train MAELoss: 0.067, Val. MSELoss: 0.00286, Val. MAELoss: 0.043\n",
      "Epoch: 30, Train MSELoss: 0.00679, Train MAELoss: 0.066, Val. MSELoss: 0.00197, Val. MAELoss: 0.035\n",
      "Epoch: 31, Train MSELoss: 0.00802, Train MAELoss: 0.071, Val. MSELoss: 0.00197, Val. MAELoss: 0.036\n",
      "Epoch: 32, Train MSELoss: 0.00602, Train MAELoss: 0.062, Val. MSELoss: 0.00204, Val. MAELoss: 0.035\n",
      "Epoch: 33, Train MSELoss: 0.00593, Train MAELoss: 0.060, Val. MSELoss: 0.00186, Val. MAELoss: 0.034\n",
      "Epoch: 34, Train MSELoss: 0.00534, Train MAELoss: 0.058, Val. MSELoss: 0.00211, Val. MAELoss: 0.036\n",
      "Epoch: 35, Train MSELoss: 0.00520, Train MAELoss: 0.059, Val. MSELoss: 0.00217, Val. MAELoss: 0.036\n",
      "Epoch: 36, Train MSELoss: 0.00484, Train MAELoss: 0.055, Val. MSELoss: 0.00188, Val. MAELoss: 0.035\n",
      "Epoch: 37, Train MSELoss: 0.00463, Train MAELoss: 0.054, Val. MSELoss: 0.00184, Val. MAELoss: 0.034\n",
      "Epoch: 38, Train MSELoss: 0.00467, Train MAELoss: 0.054, Val. MSELoss: 0.00180, Val. MAELoss: 0.033\n",
      "Epoch: 39, Train MSELoss: 0.00443, Train MAELoss: 0.053, Val. MSELoss: 0.00181, Val. MAELoss: 0.034\n",
      "Epoch: 40, Train MSELoss: 0.00427, Train MAELoss: 0.053, Val. MSELoss: 0.00172, Val. MAELoss: 0.033\n",
      "Epoch: 41, Train MSELoss: 0.00417, Train MAELoss: 0.052, Val. MSELoss: 0.00207, Val. MAELoss: 0.035\n",
      "Epoch: 42, Train MSELoss: 0.00435, Train MAELoss: 0.053, Val. MSELoss: 0.00168, Val. MAELoss: 0.033\n",
      "Epoch: 43, Train MSELoss: 0.00456, Train MAELoss: 0.054, Val. MSELoss: 0.00384, Val. MAELoss: 0.052\n",
      "Epoch: 44, Train MSELoss: 0.00426, Train MAELoss: 0.052, Val. MSELoss: 0.00168, Val. MAELoss: 0.032\n",
      "Epoch: 45, Train MSELoss: 0.00382, Train MAELoss: 0.050, Val. MSELoss: 0.00197, Val. MAELoss: 0.034\n",
      "Epoch: 46, Train MSELoss: 0.00376, Train MAELoss: 0.050, Val. MSELoss: 0.00274, Val. MAELoss: 0.042\n",
      "Epoch: 47, Train MSELoss: 0.00378, Train MAELoss: 0.050, Val. MSELoss: 0.00291, Val. MAELoss: 0.044\n",
      "Epoch: 48, Train MSELoss: 0.00399, Train MAELoss: 0.050, Val. MSELoss: 0.00166, Val. MAELoss: 0.032\n",
      "Epoch: 49, Train MSELoss: 0.00369, Train MAELoss: 0.049, Val. MSELoss: 0.00163, Val. MAELoss: 0.032\n",
      "Epoch: 50, Train MSELoss: 0.00359, Train MAELoss: 0.048, Val. MSELoss: 0.00157, Val. MAELoss: 0.031\n",
      "Epoch: 51, Train MSELoss: 0.00332, Train MAELoss: 0.046, Val. MSELoss: 0.00222, Val. MAELoss: 0.037\n",
      "Epoch: 52, Train MSELoss: 0.00348, Train MAELoss: 0.046, Val. MSELoss: 0.00174, Val. MAELoss: 0.032\n",
      "Epoch: 53, Train MSELoss: 0.00367, Train MAELoss: 0.048, Val. MSELoss: 0.00161, Val. MAELoss: 0.031\n",
      "Epoch: 54, Train MSELoss: 0.00383, Train MAELoss: 0.048, Val. MSELoss: 0.00327, Val. MAELoss: 0.048\n",
      "Epoch: 55, Train MSELoss: 0.00322, Train MAELoss: 0.045, Val. MSELoss: 0.00178, Val. MAELoss: 0.034\n",
      "Epoch: 56, Train MSELoss: 0.00360, Train MAELoss: 0.048, Val. MSELoss: 0.00168, Val. MAELoss: 0.032\n",
      "Epoch: 57, Train MSELoss: 0.00351, Train MAELoss: 0.047, Val. MSELoss: 0.00314, Val. MAELoss: 0.046\n",
      "Epoch: 58, Train MSELoss: 0.00349, Train MAELoss: 0.047, Val. MSELoss: 0.00182, Val. MAELoss: 0.033\n",
      "Epoch: 59, Train MSELoss: 0.00314, Train MAELoss: 0.045, Val. MSELoss: 0.00186, Val. MAELoss: 0.034\n",
      "Epoch: 60, Train MSELoss: 0.00304, Train MAELoss: 0.043, Val. MSELoss: 0.00165, Val. MAELoss: 0.032\n",
      "Epoch: 61, Train MSELoss: 0.00394, Train MAELoss: 0.048, Val. MSELoss: 0.00182, Val. MAELoss: 0.033\n",
      "Epoch: 62, Train MSELoss: 0.00316, Train MAELoss: 0.045, Val. MSELoss: 0.00177, Val. MAELoss: 0.032\n",
      "Epoch: 63, Train MSELoss: 0.00294, Train MAELoss: 0.043, Val. MSELoss: 0.00212, Val. MAELoss: 0.036\n",
      "Epoch: 64, Train MSELoss: 0.00301, Train MAELoss: 0.044, Val. MSELoss: 0.00146, Val. MAELoss: 0.031\n",
      "Epoch: 65, Train MSELoss: 0.00299, Train MAELoss: 0.043, Val. MSELoss: 0.00156, Val. MAELoss: 0.030\n",
      "Epoch: 66, Train MSELoss: 0.00299, Train MAELoss: 0.044, Val. MSELoss: 0.00256, Val. MAELoss: 0.041\n",
      "Epoch: 67, Train MSELoss: 0.00332, Train MAELoss: 0.046, Val. MSELoss: 0.00287, Val. MAELoss: 0.044\n",
      "Epoch: 68, Train MSELoss: 0.00314, Train MAELoss: 0.045, Val. MSELoss: 0.00148, Val. MAELoss: 0.030\n",
      "Epoch: 69, Train MSELoss: 0.00345, Train MAELoss: 0.045, Val. MSELoss: 0.00212, Val. MAELoss: 0.036\n",
      "Epoch: 70, Train MSELoss: 0.00297, Train MAELoss: 0.044, Val. MSELoss: 0.00248, Val. MAELoss: 0.041\n",
      "Epoch: 71, Train MSELoss: 0.00295, Train MAELoss: 0.044, Val. MSELoss: 0.00225, Val. MAELoss: 0.038\n",
      "Epoch: 72, Train MSELoss: 0.00267, Train MAELoss: 0.041, Val. MSELoss: 0.00239, Val. MAELoss: 0.040\n",
      "Epoch: 73, Train MSELoss: 0.00286, Train MAELoss: 0.043, Val. MSELoss: 0.00163, Val. MAELoss: 0.031\n",
      "Epoch: 74, Train MSELoss: 0.00271, Train MAELoss: 0.041, Val. MSELoss: 0.00136, Val. MAELoss: 0.029\n",
      "Epoch: 75, Train MSELoss: 0.00264, Train MAELoss: 0.041, Val. MSELoss: 0.00143, Val. MAELoss: 0.030\n",
      "Epoch: 76, Train MSELoss: 0.00301, Train MAELoss: 0.043, Val. MSELoss: 0.00144, Val. MAELoss: 0.029\n",
      "Epoch: 77, Train MSELoss: 0.00272, Train MAELoss: 0.041, Val. MSELoss: 0.00143, Val. MAELoss: 0.029\n",
      "Epoch: 78, Train MSELoss: 0.00271, Train MAELoss: 0.041, Val. MSELoss: 0.00134, Val. MAELoss: 0.028\n",
      "Epoch: 79, Train MSELoss: 0.00263, Train MAELoss: 0.040, Val. MSELoss: 0.00180, Val. MAELoss: 0.033\n",
      "Epoch: 80, Train MSELoss: 0.00275, Train MAELoss: 0.041, Val. MSELoss: 0.00169, Val. MAELoss: 0.031\n",
      "Epoch: 81, Train MSELoss: 0.00313, Train MAELoss: 0.043, Val. MSELoss: 0.00214, Val. MAELoss: 0.036\n",
      "Epoch: 82, Train MSELoss: 0.00269, Train MAELoss: 0.040, Val. MSELoss: 0.00266, Val. MAELoss: 0.043\n",
      "Epoch: 83, Train MSELoss: 0.00246, Train MAELoss: 0.040, Val. MSELoss: 0.00128, Val. MAELoss: 0.028\n",
      "Epoch: 84, Train MSELoss: 0.00267, Train MAELoss: 0.041, Val. MSELoss: 0.00152, Val. MAELoss: 0.030\n",
      "Epoch: 85, Train MSELoss: 0.00280, Train MAELoss: 0.042, Val. MSELoss: 0.00134, Val. MAELoss: 0.028\n",
      "Epoch: 86, Train MSELoss: 0.00246, Train MAELoss: 0.039, Val. MSELoss: 0.00126, Val. MAELoss: 0.028\n",
      "Epoch: 87, Train MSELoss: 0.00238, Train MAELoss: 0.039, Val. MSELoss: 0.00174, Val. MAELoss: 0.032\n",
      "Epoch: 88, Train MSELoss: 0.00271, Train MAELoss: 0.041, Val. MSELoss: 0.00127, Val. MAELoss: 0.028\n",
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      "Epoch: 717, Train MSELoss: 0.00147, Train MAELoss: 0.030, Val. MSELoss: 0.00106, Val. MAELoss: 0.024\n",
      "Epoch: 718, Train MSELoss: 0.00137, Train MAELoss: 0.028, Val. MSELoss: 0.00108, Val. MAELoss: 0.025\n",
      "Epoch: 719, Train MSELoss: 0.00137, Train MAELoss: 0.028, Val. MSELoss: 0.00094, Val. MAELoss: 0.023\n",
      "Epoch: 720, Train MSELoss: 0.00135, Train MAELoss: 0.028, Val. MSELoss: 0.00091, Val. MAELoss: 0.024\n",
      "Epoch: 721, Train MSELoss: 0.00171, Train MAELoss: 0.032, Val. MSELoss: 0.00120, Val. MAELoss: 0.026\n",
      "Epoch: 722, Train MSELoss: 0.00161, Train MAELoss: 0.031, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 723, Train MSELoss: 0.00143, Train MAELoss: 0.029, Val. MSELoss: 0.00106, Val. MAELoss: 0.024\n",
      "Epoch: 724, Train MSELoss: 0.00146, Train MAELoss: 0.030, Val. MSELoss: 0.00086, Val. MAELoss: 0.022\n",
      "Epoch: 725, Train MSELoss: 0.00148, Train MAELoss: 0.029, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 726, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00084, Val. MAELoss: 0.023\n",
      "Epoch: 727, Train MSELoss: 0.00153, Train MAELoss: 0.030, Val. MSELoss: 0.00154, Val. MAELoss: 0.032\n",
      "Epoch: 728, Train MSELoss: 0.00154, Train MAELoss: 0.030, Val. MSELoss: 0.00087, Val. MAELoss: 0.022\n",
      "Epoch: 729, Train MSELoss: 0.00151, Train MAELoss: 0.030, Val. MSELoss: 0.00084, Val. MAELoss: 0.022\n",
      "Epoch: 730, Train MSELoss: 0.00168, Train MAELoss: 0.031, Val. MSELoss: 0.00120, Val. MAELoss: 0.026\n",
      "Epoch: 731, Train MSELoss: 0.00147, Train MAELoss: 0.030, Val. MSELoss: 0.00089, Val. MAELoss: 0.023\n",
      "Epoch: 732, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00090, Val. MAELoss: 0.023\n",
      "Epoch: 733, Train MSELoss: 0.00136, Train MAELoss: 0.028, Val. MSELoss: 0.00095, Val. MAELoss: 0.023\n",
      "Epoch: 734, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00087, Val. MAELoss: 0.023\n",
      "Epoch: 735, Train MSELoss: 0.00149, Train MAELoss: 0.029, Val. MSELoss: 0.00091, Val. MAELoss: 0.023\n",
      "Epoch: 736, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00121, Val. MAELoss: 0.027\n",
      "Epoch: 737, Train MSELoss: 0.00153, Train MAELoss: 0.030, Val. MSELoss: 0.00084, Val. MAELoss: 0.023\n",
      "Epoch: 738, Train MSELoss: 0.00160, Train MAELoss: 0.031, Val. MSELoss: 0.00194, Val. MAELoss: 0.037\n",
      "Epoch: 739, Train MSELoss: 0.00193, Train MAELoss: 0.035, Val. MSELoss: 0.00125, Val. MAELoss: 0.027\n",
      "Epoch: 740, Train MSELoss: 0.00159, Train MAELoss: 0.031, Val. MSELoss: 0.00088, Val. MAELoss: 0.023\n",
      "Epoch: 741, Train MSELoss: 0.00139, Train MAELoss: 0.028, Val. MSELoss: 0.00095, Val. MAELoss: 0.023\n",
      "Epoch: 742, Train MSELoss: 0.00145, Train MAELoss: 0.029, Val. MSELoss: 0.00095, Val. MAELoss: 0.024\n",
      "Epoch: 743, Train MSELoss: 0.00168, Train MAELoss: 0.032, Val. MSELoss: 0.00084, Val. MAELoss: 0.023\n",
      "Epoch: 744, Train MSELoss: 0.00152, Train MAELoss: 0.030, Val. MSELoss: 0.00134, Val. MAELoss: 0.029\n",
      "Epoch: 745, Train MSELoss: 0.00152, Train MAELoss: 0.030, Val. MSELoss: 0.00084, Val. MAELoss: 0.022\n",
      "Epoch: 746, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00108, Val. MAELoss: 0.025\n",
      "Epoch: 747, Train MSELoss: 0.00157, Train MAELoss: 0.031, Val. MSELoss: 0.00105, Val. MAELoss: 0.024\n",
      "Epoch: 748, Train MSELoss: 0.00184, Train MAELoss: 0.032, Val. MSELoss: 0.00087, Val. MAELoss: 0.023\n",
      "Epoch: 749, Train MSELoss: 0.00139, Train MAELoss: 0.029, Val. MSELoss: 0.00125, Val. MAELoss: 0.027\n",
      "Epoch: 750, Train MSELoss: 0.00153, Train MAELoss: 0.030, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 751, Train MSELoss: 0.00136, Train MAELoss: 0.028, Val. MSELoss: 0.00141, Val. MAELoss: 0.030\n",
      "Epoch: 752, Train MSELoss: 0.00148, Train MAELoss: 0.029, Val. MSELoss: 0.00094, Val. MAELoss: 0.023\n",
      "Epoch: 753, Train MSELoss: 0.00145, Train MAELoss: 0.029, Val. MSELoss: 0.00124, Val. MAELoss: 0.027\n",
      "Epoch: 754, Train MSELoss: 0.00148, Train MAELoss: 0.030, Val. MSELoss: 0.00101, Val. MAELoss: 0.024\n",
      "Epoch: 934, Train MSELoss: 0.00145, Train MAELoss: 0.029, Val. MSELoss: 0.00084, Val. MAELoss: 0.022\n",
      "Epoch: 935, Train MSELoss: 0.00146, Train MAELoss: 0.029, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 936, Train MSELoss: 0.00137, Train MAELoss: 0.028, Val. MSELoss: 0.00095, Val. MAELoss: 0.023\n",
      "Epoch: 937, Train MSELoss: 0.00145, Train MAELoss: 0.029, Val. MSELoss: 0.00100, Val. MAELoss: 0.024\n",
      "Epoch: 938, Train MSELoss: 0.00133, Train MAELoss: 0.028, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 939, Train MSELoss: 0.00131, Train MAELoss: 0.027, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 940, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 941, Train MSELoss: 0.00143, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 942, Train MSELoss: 0.00144, Train MAELoss: 0.029, Val. MSELoss: 0.00094, Val. MAELoss: 0.023\n",
      "Epoch: 943, Train MSELoss: 0.00134, Train MAELoss: 0.028, Val. MSELoss: 0.00088, Val. MAELoss: 0.022\n",
      "Epoch: 944, Train MSELoss: 0.00137, Train MAELoss: 0.028, Val. MSELoss: 0.00101, Val. MAELoss: 0.024\n",
      "Epoch: 945, Train MSELoss: 0.00140, Train MAELoss: 0.029, Val. MSELoss: 0.00099, Val. MAELoss: 0.023\n",
      "Epoch: 946, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00086, Val. MAELoss: 0.022\n",
      "Epoch: 947, Train MSELoss: 0.00131, Train MAELoss: 0.027, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 948, Train MSELoss: 0.00145, Train MAELoss: 0.029, Val. MSELoss: 0.00090, Val. MAELoss: 0.023\n",
      "Epoch: 949, Train MSELoss: 0.00158, Train MAELoss: 0.031, Val. MSELoss: 0.00145, Val. MAELoss: 0.030\n",
      "Epoch: 950, Train MSELoss: 0.00156, Train MAELoss: 0.031, Val. MSELoss: 0.00121, Val. MAELoss: 0.027\n",
      "Epoch: 951, Train MSELoss: 0.00150, Train MAELoss: 0.030, Val. MSELoss: 0.00084, Val. MAELoss: 0.022\n",
      "Epoch: 952, Train MSELoss: 0.00149, Train MAELoss: 0.029, Val. MSELoss: 0.00140, Val. MAELoss: 0.029\n",
      "Epoch: 953, Train MSELoss: 0.00140, Train MAELoss: 0.028, Val. MSELoss: 0.00099, Val. MAELoss: 0.024\n",
      "Epoch: 954, Train MSELoss: 0.00144, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 955, Train MSELoss: 0.00133, Train MAELoss: 0.028, Val. MSELoss: 0.00086, Val. MAELoss: 0.023\n",
      "Epoch: 956, Train MSELoss: 0.00140, Train MAELoss: 0.028, Val. MSELoss: 0.00108, Val. MAELoss: 0.025\n",
      "Epoch: 957, Train MSELoss: 0.00140, Train MAELoss: 0.028, Val. MSELoss: 0.00118, Val. MAELoss: 0.026\n",
      "Epoch: 958, Train MSELoss: 0.00149, Train MAELoss: 0.029, Val. MSELoss: 0.00088, Val. MAELoss: 0.023\n",
      "Epoch: 959, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00151, Val. MAELoss: 0.031\n",
      "Epoch: 960, Train MSELoss: 0.00146, Train MAELoss: 0.030, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 961, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00084, Val. MAELoss: 0.022\n",
      "Epoch: 962, Train MSELoss: 0.00154, Train MAELoss: 0.030, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 963, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00088, Val. MAELoss: 0.023\n",
      "Epoch: 964, Train MSELoss: 0.00139, Train MAELoss: 0.028, Val. MSELoss: 0.00113, Val. MAELoss: 0.025\n",
      "Epoch: 965, Train MSELoss: 0.00140, Train MAELoss: 0.028, Val. MSELoss: 0.00088, Val. MAELoss: 0.022\n",
      "Epoch: 966, Train MSELoss: 0.00147, Train MAELoss: 0.029, Val. MSELoss: 0.00114, Val. MAELoss: 0.025\n",
      "Epoch: 967, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 968, Train MSELoss: 0.00134, Train MAELoss: 0.028, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 969, Train MSELoss: 0.00179, Train MAELoss: 0.033, Val. MSELoss: 0.00109, Val. MAELoss: 0.025\n",
      "Epoch: 970, Train MSELoss: 0.00149, Train MAELoss: 0.030, Val. MSELoss: 0.00097, Val. MAELoss: 0.023\n",
      "Epoch: 971, Train MSELoss: 0.00159, Train MAELoss: 0.031, Val. MSELoss: 0.00086, Val. MAELoss: 0.022\n",
      "Epoch: 972, Train MSELoss: 0.00135, Train MAELoss: 0.028, Val. MSELoss: 0.00149, Val. MAELoss: 0.031\n",
      "Epoch: 973, Train MSELoss: 0.00162, Train MAELoss: 0.032, Val. MSELoss: 0.00143, Val. MAELoss: 0.030\n",
      "Epoch: 974, Train MSELoss: 0.00141, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 975, Train MSELoss: 0.00135, Train MAELoss: 0.028, Val. MSELoss: 0.00086, Val. MAELoss: 0.022\n",
      "Epoch: 976, Train MSELoss: 0.00141, Train MAELoss: 0.029, Val. MSELoss: 0.00114, Val. MAELoss: 0.025\n",
      "Epoch: 977, Train MSELoss: 0.00148, Train MAELoss: 0.029, Val. MSELoss: 0.00124, Val. MAELoss: 0.027\n",
      "Epoch: 978, Train MSELoss: 0.00143, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 979, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00099, Val. MAELoss: 0.023\n",
      "Epoch: 980, Train MSELoss: 0.00141, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 981, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 982, Train MSELoss: 0.00147, Train MAELoss: 0.029, Val. MSELoss: 0.00096, Val. MAELoss: 0.023\n",
      "Epoch: 983, Train MSELoss: 0.00135, Train MAELoss: 0.028, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 984, Train MSELoss: 0.00139, Train MAELoss: 0.029, Val. MSELoss: 0.00085, Val. MAELoss: 0.023\n",
      "Epoch: 985, Train MSELoss: 0.00149, Train MAELoss: 0.030, Val. MSELoss: 0.00098, Val. MAELoss: 0.023\n",
      "Epoch: 986, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00085, Val. MAELoss: 0.022\n",
      "Epoch: 987, Train MSELoss: 0.00151, Train MAELoss: 0.030, Val. MSELoss: 0.00120, Val. MAELoss: 0.026\n",
      "Epoch: 988, Train MSELoss: 0.00157, Train MAELoss: 0.030, Val. MSELoss: 0.00097, Val. MAELoss: 0.023\n",
      "Epoch: 989, Train MSELoss: 0.00130, Train MAELoss: 0.027, Val. MSELoss: 0.00087, Val. MAELoss: 0.023\n",
      "Epoch: 990, Train MSELoss: 0.00146, Train MAELoss: 0.029, Val. MSELoss: 0.00094, Val. MAELoss: 0.023\n",
      "Epoch: 991, Train MSELoss: 0.00134, Train MAELoss: 0.028, Val. MSELoss: 0.00086, Val. MAELoss: 0.023\n",
      "Epoch: 992, Train MSELoss: 0.00138, Train MAELoss: 0.028, Val. MSELoss: 0.00154, Val. MAELoss: 0.031\n",
      "Epoch: 993, Train MSELoss: 0.00144, Train MAELoss: 0.029, Val. MSELoss: 0.00104, Val. MAELoss: 0.024\n",
      "Epoch: 994, Train MSELoss: 0.00136, Train MAELoss: 0.028, Val. MSELoss: 0.00107, Val. MAELoss: 0.025\n",
      "Epoch: 995, Train MSELoss: 0.00134, Train MAELoss: 0.027, Val. MSELoss: 0.00119, Val. MAELoss: 0.026\n",
      "Epoch: 996, Train MSELoss: 0.00143, Train MAELoss: 0.029, Val. MSELoss: 0.00089, Val. MAELoss: 0.023\n",
      "Epoch: 997, Train MSELoss: 0.00139, Train MAELoss: 0.028, Val. MSELoss: 0.00113, Val. MAELoss: 0.025\n",
      "Epoch: 998, Train MSELoss: 0.00142, Train MAELoss: 0.029, Val. MSELoss: 0.00127, Val. MAELoss: 0.027\n",
      "Epoch: 999, Train MSELoss: 0.00141, Train MAELoss: 0.029, Val. MSELoss: 0.00092, Val. MAELoss: 0.023\n",
      "Epoch: 1000, Train MSELoss: 0.00149, Train MAELoss: 0.029, Val. MSELoss: 0.00093, Val. MAELoss: 0.023\n"
     ]
    }
   ],
   "source": [
    "epoch = 1000\n",
    "train_mselosses = []\n",
    "valid_mselosses = []\n",
    "train_maelosses = []\n",
    "valid_maelosses = []\n",
    "\n",
    "for epoch in range(epoch):\n",
    "    train_loss_mse, train_loss_mae = train(model, train_loader, optimizer)\n",
    "    valid_loss_mse, valid_loss_mae = evaluate(model, valid_loader)\n",
    "    \n",
    "    train_mselosses.append(train_loss_mse)\n",
    "    valid_mselosses.append(valid_loss_mse)\n",
    "    train_maelosses.append(train_loss_mae)\n",
    "    valid_maelosses.append(valid_loss_mae)\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02}, Train MSELoss: {train_loss_mse:.5f}, Train MAELoss: {train_loss_mae:.3f}, Val. MSELoss: {valid_loss_mse:.5f}, Val. MAELoss: {valid_loss_mae:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c5c1b8-1851-4845-a1bf-a324eeb25930",
   "metadata": {},
   "source": [
    "Plotting training and validation loss over epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "760cfece-1586-4f1b-8be5-6a5d1dbc32ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制 MSE损失图\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_mselosses, label='Train MSELoss')\n",
    "plt.plot(valid_mselosses, label='Validation MSELoss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MSELoss')\n",
    "plt.title('Train and Validation MSELoss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# 绘制 MAE损失图\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_maelosses, label='Train MAELoss')\n",
    "plt.plot(valid_maelosses, label='Validation MAELoss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAELoss')\n",
    "plt.title('Train and Validation MAELoss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2320df4d-34d9-423a-baae-c3a4eba4243f",
   "metadata": {},
   "source": [
    "**Model evaluation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb05680-5029-43d5-8603-206b83bfb03c",
   "metadata": {},
   "source": [
    "定义预测函数 `prediction` 以方便调用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "033d6a20-c878-4a5a-9ccd-061146dd5f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义 prediction函数\n",
    "def prediction(model, iterator): \n",
    "    all_targets = []\n",
    "    all_predictions = []\n",
    "\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            inputs, targets = batch\n",
    "            predictions = model(inputs)\n",
    "            \n",
    "            all_targets.extend(targets.numpy())\n",
    "            all_predictions.extend(predictions.numpy())\n",
    "    return all_targets, all_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d69be6-a911-4cc9-a6c3-ab6870df76f3",
   "metadata": {},
   "source": [
    "这段代码定义了一个名为 `prediction` 的函数，其主要目的是使用给定的模型对输入数据进行预测，并收集所有的目标值和预测值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251b793b-20c8-4a73-bcb0-88490d206204",
   "metadata": {},
   "source": [
    "**验证集预测**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2fd8a57-d32a-48dd-aa71-6ba02a772036",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型预测\n",
    "targets, predictions = prediction(model, valid_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36a30856-f88b-4455-9815-75381fd901ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "denormalized_targets = scaler.inverse_transform(targets)\n",
    "denormalized_predictions = scaler.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03068da2-c682-4f29-bb00-1de70cb1c915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[171.51489212],\n",
       "       [169.98884435],\n",
       "       [170.24818359],\n",
       "       [170.7668437 ],\n",
       "       [173.30024699],\n",
       "       [171.95373507],\n",
       "       [173.21049513],\n",
       "       [174.45726084],\n",
       "       [177.0305773 ],\n",
       "       [178.52670166]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denormalized_targets[0:10, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fdb34f40-c63c-4d41-9d4e-23fc325dd5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.style.use('_mpl-gallery')\n",
    "plt.title('Comparison of validation set prediction results')\n",
    "plt.plot(denormalized_targets, color='blue',label='Actual Value')\n",
    "plt.plot(denormalized_predictions, color='orange', label='Valid Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b430808-b522-4951-9622-c17f29143c25",
   "metadata": {},
   "source": [
    "**回归拟合图**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "064df01a-a5a3-49e5-b84e-a669a5d94464",
   "metadata": {},
   "source": [
    "`regplot()` 函数绘制数据图并拟合线性回归模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "363da4ac-7a1e-4f29-8854-e28386e4e7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "sns.regplot(x=denormalized_targets, y=denormalized_predictions, scatter=True, marker=\"*\", color='orange',line_kws={'color': 'red'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c2a77b-921c-4007-aa36-b3a781a99a59",
   "metadata": {},
   "source": [
    "**评估指标**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49242ad7-b1fa-495f-a155-3f57f5b2d8d9",
   "metadata": {},
   "source": [
    "通过调用 `sklearn.metrics` 模块中的 `mean_absolute_error` `mean_absolute_percentage_error` `mean_squared_error` `root_mean_squared_error` `r2_score` 函数来评估模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f6b2984-df04-45c6-879c-bff640996c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.0229\n",
      "MAPE: 3.0055%\n",
      "MSE: 0.0009\n",
      "RMSE: 0.0302\n",
      "R²: 0.9346\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(targets, predictions)\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "\n",
    "mape = mean_absolute_percentage_error(targets, predictions)\n",
    "print(f\"MAPE: {mape * 100:.4f}%\")\n",
    "\n",
    "mse = mean_squared_error(targets, predictions)\n",
    "print(f\"MSE: {mse:.4f}\")\n",
    "\n",
    "rmse = root_mean_squared_error(targets, predictions)\n",
    "print(f\"RMSE: {rmse:.4f}\")\n",
    "\n",
    "r2 = r2_score(targets, predictions)\n",
    "print(f\"R²: {r2:.4f}\")"
   ]
  },
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   "id": "da5f02f4-1cac-4d92-b869-ee535945609c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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